Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique

Saghir Ahmed; Basit Raza; Lal Hussain; Touseef Sadiq; Ashit Kumar Dutta

doi:10.1371/journal.pone.0310011

Peer Review History

Original SubmissionMay 29, 2024
22 Jul 2024 Decision Letter - Catalin Buiu, Editor PONE-D-24-21569Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection TechniquePLOS ONE Dear Dr. Hussain, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Sep 05 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Catalin Buiu Academic Editor PLOS ONE Journal Requirements: 1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. Please provide a complete Data Availability Statement in the submission form, ensuring you include all necessary access information or a reason for why you are unable to make your data freely accessible. If your research concerns only data provided within your submission, please write "All data are in the manuscript and/or supporting information files" as your Data Availability Statement. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: I Don't Know Reviewer #2: Yes Reviewer #3: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes Reviewer #3: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No Reviewer #3: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Authors should address the following major revisions: 1) These research, by virtue of the fact, stress the existence and severity of dire results associated with delayed and improper diagnosis which can be a real threat to the survival of humankind. The latest studies have revealed that deep learning models can be used for coronavirus image classification.- add the following work for this statement such as: A Deep learning-based X-Ray Imaging Diagnosis System for Classification of Tuberculosis, COVID19, and Pneumonia Traits using Evolutionary Algorithm 2) While deep learning models have shown promise in classifying COVID-19 images, there are still limitations that need to be addressed. One such limitation is the lack of research on applying domain adaptation techniques to overcome the challenge of the cross-dataset problem.- add the following work for this statement such as: Deep learning network selection and optimized information fusion for enhanced COVID-19 detection 3) Related work of this manuscript can be further enhanced by adding few recent works. Also, discuss the cutting edges and gaps that should be linked with the proposed work. 4) How many features are extracted from the VGG model? How many layers used for the re-training on the selected datasets? 5) how many parameters are trained for the VGG model and how the hyperparameters are selected? add in the manuscript. 6) What is the purpose of GLCM features and how many features are extracted? 7) What is the loss function of the XGBosst classifier and what is the purpose of this classifier? Reviewer #2: The paper proposes a novel approach called “Enhancing COVID Prediction with ESN-MDFS”, which combines an Extreme Smart Network (ESN) and a Mean Dropout Feature Selection Technique (MDFS) to improve the multi-class detection of COVID-19 and three other lung conditions (normal, viral, and bacterial) using portable chest x-rays (CXRs). This method extracts both static features using Grey-level Co-occurrence Matrix (GLCM) texture analysis and dynamic features through a VGG-16 pre-trained deep learning model. The authors highlight the limitations of traditional diagnostic methods, such as RT-PCR and biomarkers. Their study seeks to overcome challenges like data variability and the need for large labeled datasets by developing a robust, lightweight deep learning model for COVID-19 detection. The authors present the key steps of the proposed method, namely: 1. Preprocessing: Images are resized, denoised using median blur, and normalized for intensity. 2. Data Augmentation: Techniques like rotation, flipping, shearing, contrast adjustments, and various transforms are applied to balance the dataset and prevent overfitting. 3. Feature Selection: Static Features are extracted using Grey Level Co-occurrence Matrix (GLCM) analysis, while Dynamic Features are extracted using a pre-trained VGG-16 model. The Hybrid Feature Space combines static and dynamic features, optimized using the Mean Dropout Feature Selection Technique (MDFS). 4. Model Training: The hybrid feature space is used to train an XGBoost model, evaluated using precision, recall, and F1-score metrics. The proposed model demonstrated high performance in multi-class classification of lung conditions, achieving an accuracy of 95.54% without mean dropout and 96.18% with mean dropout. High scores were achieved for precision, recall, and F1-score across all four classes (bacterial pneumonia, COVID-19, normal lungs, and viral pneumonia). The reported Area Under the Curve (AUC) values were 0.99 for bacterial and viral pneumonia, 0.96 for normal, and 0.95 for COVID-19. The ESN-MDFS model surpassed existing lightweight models in both accuracy (achieving 96.18% compared to the next best at 95.54%) and compactness (889 KB compared to the next best at 12 MB). The authors demonstrated that the proposed model is ideal for deployment on resource-constrained edge devices, facilitating immediate diagnostic insights and more rapid treatment decisions. Suggestions: 1. Review the paper for grammatical errors and ensure subject-verb agreement and correct tense usage throughout the manuscript. 2. Ensure that all terms and concepts are clearly defined and consistently used throughout the paper. Consider adding a table of abbreviations to improve comprehensibility and avoid ambiguity. 3. Be consistent when reporting results. Use the same format for all metrics and comparisons. For example, in Table 5, ensure that the number of samples in each category is presented consistently across all discussed models. 4. Outline potential future work, including improvements to the model and adaptation to other imaging modalities. Reviewer #3: 1. The paper's formatting needs improvement for consistency. Please ensure that the table formatting is uniform throughout the document. Additionally, standardize the captions for figures and tables to maintain a cohesive style. Lastly, align the equations consistently, either centering them or left-aligning them. 2. The comparasion experiments should be run multiple times to show the mean and std of the results. 3. Considering that VGG16 is a well-known model, it may not be necessary to include the entire model architecture as a figure or provide extensive introductions to it. Consider simplifying this section to focus on the more novel aspects of your work. 4. While using a fixed model for feature extraction is a common technique, the concepts of "Dynamic Features" and "Static Features" in this context are unclear. Please provide a clear definition of what you consider dynamic and static features and how they relate to your proposed method. 5. The baseline methods compared in this paper appear to be outdated, with references [61]-[63] being papers published in 2020. Given the rapid advancements in this field, it would be beneficial to compare your work with more recent publications. For example, consider including a comparison with the work by Pratiwi et al. (2021), "Effect of CT-scan image resizing, enhancement and normalization on accuracy of covid-19 detection," which also utilizes VGG16. Providing a justification for not comparing with more recent works would strengthen your paper. 6. Lack of ablation studies. The paper would benefit from the inclusion of ablation studies to demonstrate the impact of each module in the proposed framework. 7. The novelty of the proposed method is not clearly evident. The combination of data augmentation, using a fixed CNN model for feature extraction, and training an SVM or XGBoost classifier is a well-established and widely used technique across various fields. To enhance the contribution of this paper, consider highlighting the specific innovations or improvements your method offers compared to existing approaches. Clearly articulate how your proposed framework advances the state-of-the-art or addresses limitations of previous methods in the context of your specific application. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0310011.r001
Revision 1
2 Aug 2024 Author Response To: Prof. Dr. Joerg Heber PLOS ONE, Editor-in-Chief Re: Manuscript submission to PLOS ONE PONE-D-24-21569 Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection Technique Date: July 31, 2024 Dear Dr. Heber We are pleased to inform you that we have revised the manuscript in the light of reviewers’ comments. The reviewers’ recommendations were extremely useful, and we have addressed all their recommendations in the revised manuscript. Please see below for responses to each individual comment. We hope that the revisions in the manuscript and our accompanying responses will be sufficient to make our manuscript suitable for publication in PLOS ONE. S. No. Comments Rebuttal Reviewer 1 Comments 1 This research, by virtue of the fact, stress the existence and severity of dire results associated with delayed and improper diagnosis which can be a real threat to the survival of humankind. The latest studies have revealed that deep learning models can be used for coronavirus image classification. - add the following work for this statement such as: A Deep learning-based X-Ray Imaging Diagnosis System for Classification of Tuberculosis, COVID19, and Pneumonia Traits using Evolutionary Algorithm The authors [15] proposed a novel automated framework for the classification of tuberculosis, COVID-19, and pneumonia from chest x-ray images using deep learning and an improved optimization technique. The proposed deep learning-based framework achieved high classification accuracy 98.2%, 99.0%, and 98.7%) on three different datasets for tuberculosis, COVID-19, and pneumonia detection from chest X-ray images. The authors employed the Wilcoxon signed-rank test to statistically validate the superior performance of their proposed method. The integration of feature fusion was instrumental in enhancing the method's accuracy. 2 While deep learning models have shown promise in classifying COVID-19 images, there are still limitations that need to be addressed. One such limitation is the lack of research on applying domain adaptation techniques to overcome the challenge of the cross-dataset problem. - add the following work for this statement such as: Deep learning network selection and optimized information fusion for enhanced COVID-19 detection The researchers [16] proposed a wrapper-based technique to improve the classification performance of chest infection (including COVID-19) detection using X-rays by extracting deep features using pretrained deep learning models and optimizing them using various optimization techniques, while also using a network selection technique to select the deep learning models. The proposed deep learning framework achieved a high classification accuracy of 97.7% in detecting chest infections, including COVID-19. Rigorous validation confirmed the framework's reliability for classifying both COVID-19 and other chest infections, suggesting its potential as a valuable tool for clinicians. 3 Related work of this manuscript can be further enhanced by adding few recent works. Also, discuss the cutting edges and gaps that should be linked with the proposed work. The issues have been addressed. Some related work from recent studies as suggested by the reviewers have been incorporated in the literature and discussion sections. Moreover, future directions also been added. 4 How many features are extracted from the VGG model? How many layers used for the re-training on the selected datasets? The VGG16 model was employed to extract 1024 features. To adapt the model to the specific task, the final four layers were fine-tuned using the selected datasets 5 What is the purpose of GLCM features and how many features are extracted? The Gray Level Co-occurrence Matrix (GLCM) is a statistical technique used to extract texture features from images by analyzing the spatial relationships between pixel intensities. Its applications span various domains, including SAR imagery for land cover classification (water, vegetation, urban areas) [48] and medical imaging for detecting retinal abnormalities [49], where color features have shown superior accuracy. GLCM calculates the relative frequency of pixel pairs with specific intensities and spatial configurations, creating a matrix from which statistical measures can be derived. For this study, we computed 25 GLCM features, subsequently reducing them to 17 using MeanDropout feature selection. 6 What is the loss function of the XGBoost classifier and what is the purpose of this classifier? XGBoost traditionally uses convex loss functions, recent research has explored custom and non-convex loss functions to enhance performance in specific applications [59]. For instance, [60] investigated the use of squared logistics loss (SqLL) to improve accuracy. [59] developed weighted softmax loss functions for industrial applications, while [61] proposed a generalized XGBoost method accommodating both convex and some non-convex loss functions. These advancements demonstrate XGBoost's versatility and potential for tailored solutions in various domains, including big data analysis and multi-objective parameter regularization. The purpose of the XGBoost classifier is multifaceted and versatile, as evidenced by various research studies. XGBoost is utilized for enhancing prediction accuracy in diverse fields such as meteorology for hailstorm forecasting [62], detecting patterns in financial datasets to differentiate between solvable and bankrupt situations [63], improving learner performance prediction in Intelligent Tutoring Systems by enhancing models like Performance Factor Analysis and DAS3H [64], and detecting malware in Internet of Medical Things (IoMT) data for better medical assistance through dimensionality reduction and efficient classification [65]. The XGBoost algorithm's scalability, robustness, and proficiency with complex datasets make it a valuable tool for increasing prediction accuracy, addressing class imbalances, enhancing performance prediction models, and improving data analysis in various domains. Reviewer 2 Comments 1 Review the paper for grammatical errors and ensure subject-verb agreement and correct tense usage throughout the manuscript. The issue has been addressed 2 Ensure that all terms and concepts are clearly defined and consistently used throughout the paper. Consider adding a table of abbreviations to improve comprehensibility and avoid ambiguity. The issue has been addressed and highlighted in red color 3 Be consistent when reporting results. Use the same format for all metrics and comparisons. For example, in Table 5, ensure that the number of samples in each category is presented consistently across all discussed models. The issue has been addressed and highlighted in red color 4 Outline potential future work, including improvements to the model and adaptation to other imaging modalities. The issue has been addressed and highlighted in red color in the introduction section Reviewer 3 Comments 1 The paper's formatting needs improvement for consistency. Please ensure that the table formatting is uniform throughout the document. Additionally, standardize the captions for figures and tables to maintain a cohesive style. Lastly, align the equations consistently, either centering them or left aligning them. The issue has been addressed 2 The comparison experiments should be run multiple times to show the mean and std of the results. Fig. 10. Fold vs accuracy curve to distinguish multi-class using ESN-MDFS Figure 10 illustrates the accuracy of the ESN-MDFS model across seven cross-validation folds. The model achieved a mean accuracy of 95.57% with a standard deviation of 0.54, demonstrating consistent performance across different data subsets. 3 Considering that VGG16 is a well-known model, it may not be necessary to include the entire model architecture as a figure or provide extensive introductions to it. Consider simplifying this section to focus on the more novel aspects of your work. The issue has been addressed and details have been incorporated according as advised by the esteemed reviewer 4 While using a fixed model for feature extraction is a common technique, the concepts of "Dynamic Features" and "Static Features" in this context are unclear. Please provide a clear definition of what you consider dynamic and static features and how they relate to your proposed method. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These features are sensitive to image patterns and structures, which can be crucial for differentiating between different types of lung abnormalities. By combining deep features and GLCM features, it is possible to create a more robust and discriminative feature space for multi-class COVID-19 classification. 5 The baseline methods compared in this paper appear to be outdated, with references [61]-[63] being papers published in 2020. Given the rapid advancements in this field, it would be beneficial to compare your work with more recent publications. For example, consider including a comparison with the work by Pratiwi et al. (2021), "Effect of CT scan image resizing, enhancement and normalization on accuracy of covid-19 detection," which also utilizes VGG16. Providing a justification for not comparing with more recent works would strengthen your paper. The primary outcome measured in this study [73] is the accuracy of COVID-19 detection using CT-scan images and various preprocessing methods. The main findings of this study are that different preprocessing methods, including resizing, enhancement, and normalization, had an impact on the accuracy of COVID-19 classification using a deep learning model (VGG-16), and the highest accuracy of 88.54% was achieved using a combination of deformed resizing, CLAHE enhancement, and normalization to the range of [0 1] and [-1 1]. Initial model comparisons were conducted using contemporary models available at the time of results generation. However, we acknowledge the value of the reviewers' suggestions to benchmark against the most recent state-of-the-art research. 6 Lack of ablation studies. The paper would benefit from the inclusion of ablation studies to demonstrate the impact of each module in the proposed framework. As per suggestion we calculate the results through VGG16 as a single module. a) b) c) d) Fig. 5 Multi-class Covid-19 detection using VGG-16, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 5 presents the multi-class COVID-19 detection results exclusively based on VGG-16 deep features, as visualized through confusion matrices (5a), classification reports (5b), AUC curves (5c), and accuracy loss curves (5d). Relying solely on deep features, the model achieved an overall accuracy of 93% in classifying the four target classes. Notably, the AUC for multi-class differentiation (bacterial, COVID-19, viral) was 0.99, while perfect discrimination (AUC of 1.00) was observed for the normal class. As per suggested, we obtain the results through XGBoost also, the results are presented follow: - a) b) c) Fig. 6 Multi-class Covid-19 detection using XGBoost, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 6 illustrates the multi-class COVID-19 detection performance solely based on XGBoost-processed static features, as depicted in the confusion matrix (6a), classification report (6b), and AUC curve (6c). Relying exclusively on static features, the model achieved an overall accuracy of 86% in classifying the four target classes. 7 The novelty of the proposed method is not clearly evident. The combination of data augmentation, using a fixed CNN model for feature extraction, and training an SVM or XGBoost classifier is a well-established and widely used technique across various fields. To enhance the contribution of this paper, consider highlighting the specific innovations or improvements your method offers compared to existing approaches. Clearly articulate how your proposed framework advances the state-of-the-art or addresses limitations of previous methods in the context of your specific application. This study enhances multiclass COVID-19 prediction through a novel approach encompassing the following key elements: • Optimized pre-processing: Chest X-ray image quality was improved using techniques such as interpolation, data cleaning, augmentation, feature engineering, image enhancement, morphological operations, segmentation, and transformation. • Feature extraction: Dynamic VGG-19 and static GLCM features were computed from multiclass data to capture diverse image characteristics. • Feature selection: A hybrid feature space (HFS) was refined using feature selection methods to eliminate redundant features, thereby improving prediction performance and model size for efficient deployment on edge devices • The optimal HFS was then utilized to the robust optimized XGBoost algorithm for improved prediction • Hyperparameter tuning: The hyperparameters of the XGBoost machine learning algorithm were meticulously optimized. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These features are sensitive to image patterns and structures, which can be crucial for differentiating between different types of lung abnormalities. By combining deep features and GLCM features, it is possible to create a more robust and discriminative feature space for multi-class COVID-19 classification. The hybrid feature space (HFS) • Deep features and GLCM features capture different aspects of image information, leading to improved classification performance. • The combination of these features can better differentiate between subtle visual patterns associated with different lung diseases. • The use of multiple feature types can help to reduce the impact of noise and variations in image quality. By effectively fusing these features and employing appropriate machine learning techniques, we developed highly accurate and reliable COVID-19 classification models. Looking forward to hearing from you. Regards, Dr. Lal Hussain Attachments Attachment Submitted filename: Final Rebuttle letter July 31.docx https://doi.org/10.1371/journal.pone.0310011.r002
7 Aug 2024 Decision Letter - Catalin Buiu, Editor PONE-D-24-21569R1Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection TechniquePLOS ONE Dear Dr. Hussain, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. You did not respond to comment no. 5 from Reviewer 1. Please address it, but in a separate paragraph. Your previous responses were appended to the original comment, making them difficult to follow. Please submit your revised manuscript by Sep 21 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Catalin Buiu Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments: You did not respond to comment no. 5 from Reviewer 1. Please address it, but in a separate paragraph. Your previous responses were appended to the original comments, making them difficult to follow. [Note: HTML markup is below. Please do not edit.] [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0310011.r003
Revision 2
8 Aug 2024 Author Response To: Prof. Dr. Joerg Heber PLOS ONE, Editor-in-Chief Re: Manuscript submission to PLOS ONE PONE-D-24-21569R1 Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection Technique Date: August 08, 2024 Dear Dr. Heber We are pleased to inform you that we have revised the manuscript in the light of reviewers’ comments. The reviewers’ recommendations were extremely useful, and we have addressed all their recommendations in the revised manuscript. Please see below for responses to each individual comment. We hope that the revisions in the manuscript and our accompanying responses will be sufficient to make our manuscript suitable for publication in PLOS ONE. Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Response: We have checked each reference and ensured that there is retracted article found. Additional Editor Comments: You did not respond to comment no. 5 from Reviewer 1. Please address it, but in a separate paragraph. Your previous responses were appended to the original comments, making them difficult to follow. Response: We have addressed the comments accordingly. Reviewers’ comments S. No. Comments Rebuttal Reviewer 1 Comments 1 This research, by virtue of the fact, stress the existence and severity of dire results associated with delayed and improper diagnosis which can be a real threat to the survival of humankind. The latest studies have revealed that deep learning models can be used for coronavirus image classification. - add the following work for this statement such as: A Deep learning-based X-Ray Imaging Diagnosis System for Classification of Tuberculosis, COVID19, and Pneumonia Traits using Evolutionary Algorithm The authors [15] proposed a novel automated framework for the classification of tuberculosis, COVID-19, and pneumonia from chest x-ray images using deep learning and an improved optimization technique. The proposed deep learning-based framework achieved high classification accuracy 98.2%, 99.0%, and 98.7%) on three different datasets for tuberculosis, COVID-19, and pneumonia detection from chest X-ray images. The authors employed the Wilcoxon signed-rank test to statistically validate the superior performance of their proposed method. The integration of feature fusion was instrumental in enhancing the method's accuracy. 2 While deep learning models have shown promise in classifying COVID-19 images, there are still limitations that need to be addressed. One such limitation is the lack of research on applying domain adaptation techniques to overcome the challenge of the cross-dataset problem. - add the following work for this statement such as: Deep learning network selection and optimized information fusion for enhanced COVID-19 detection The researchers [16] proposed a wrapper-based technique to improve the classification performance of chest infection (including COVID-19) detection using X-rays by extracting deep features using pretrained deep learning models and optimizing them using various optimization techniques, while also using a network selection technique to select the deep learning models. The proposed deep learning framework achieved a high classification accuracy of 97.7% in detecting chest infections, including COVID-19. Rigorous validation confirmed the framework's reliability for classifying both COVID-19 and other chest infections, suggesting its potential as a valuable tool for clinicians. 3 Related work of this manuscript can be further enhanced by adding few recent works. Also, discuss the cutting edges and gaps that should be linked with the proposed work. The issues have been addressed. Some related work from recent studies as suggested by the reviewers have been incorporated in the literature and discussion sections. Moreover, future directions also been added. 4 How many features are extracted from the VGG model? How many layers used for the re-training on the selected datasets? The VGG16 model was employed to extract 1024 features. To adapt the model to the specific task, the final four layers were fine-tuned using the selected datasets 5 What is the purpose of GLCM features and how many features are extracted? The Gray Level Co-occurrence Matrix (GLCM) is a statistical technique used to extract texture features from images by analyzing the spatial relationships between pixel intensities. Its applications span various domains, including SAR imagery for land cover classification (water, vegetation, urban areas) [48] and medical imaging for detecting retinal abnormalities [49], where color features have shown superior accuracy. Gray Level Co-occurrence Matrix (GLCM) analysis computes the frequency of pixel pairs with specific intensity values and spatial relationships, forming a matrix from which statistical features can be extracted. In this study, 25 GLCM features were initially calculated and subsequently reduced to 17 through MeanDropout feature selection. . 6 What is the loss function of the XGBoost classifier and what is the purpose of this classifier? XGBoost traditionally uses convex loss functions, recent research has explored custom and non-convex loss functions to enhance performance in specific applications [59]. For instance, [60] investigated the use of squared logistics loss (SqLL) to improve accuracy. [59] developed weighted softmax loss functions for industrial applications, while [61] proposed a generalized XGBoost method accommodating both convex and some non-convex loss functions. These advancements demonstrate XGBoost's versatility and potential for tailored solutions in various domains, including big data analysis and multi-objective parameter regularization. The purpose of the XGBoost classifier is multifaceted and versatile, as evidenced by various research studies. XGBoost is utilized for enhancing prediction accuracy in diverse fields such as meteorology for hailstorm forecasting [62], detecting patterns in financial datasets to differentiate between solvable and bankrupt situations [63], improving learner performance prediction in Intelligent Tutoring Systems by enhancing models like Performance Factor Analysis and DAS3H [64], and detecting malware in Internet of Medical Things (IoMT) data for better medical assistance through dimensionality reduction and efficient classification [65]. The XGBoost algorithm's scalability, robustness, and proficiency with complex datasets make it a valuable tool for increasing prediction accuracy, addressing class imbalances, enhancing performance prediction models, and improving data analysis in various domains. Reviewer 2 Comments 1 Review the paper for grammatical errors and ensure subject-verb agreement and correct tense usage throughout the manuscript. The issue has been addressed 2 Ensure that all terms and concepts are clearly defined and consistently used throughout the paper. Consider adding a table of abbreviations to improve comprehensibility and avoid ambiguity. The issue has been addressed and highlighted in red color 3 Be consistent when reporting results. Use the same format for all metrics and comparisons. For example, in Table 5, ensure that the number of samples in each category is presented consistently across all discussed models. The issue has been addressed and highlighted in red color 4 Outline potential future work, including improvements to the model and adaptation to other imaging modalities. The issue has been addressed and highlighted in red color in the introduction section Reviewer 3 Comments 1 The paper's formatting needs improvement for consistency. Please ensure that the table formatting is uniform throughout the document. Additionally, standardize the captions for figures and tables to maintain a cohesive style. Lastly, align the equations consistently, either centering them or left aligning them. The issue has been addressed 2 The comparison experiments should be run multiple times to show the mean and std of the results. Fig. 10. Fold vs accuracy curve to distinguish multi-class using ESN-MDFS Figure 10 illustrates the accuracy of the ESN-MDFS model across seven cross-validation folds. The model achieved a mean accuracy of 95.57% with a standard deviation of 0.54, demonstrating consistent performance across different data subsets. 3 Considering that VGG16 is a well-known model, it may not be necessary to include the entire model architecture as a figure or provide extensive introductions to it. Consider simplifying this section to focus on the more novel aspects of your work. The issue has been addressed and details have been incorporated according as advised by the esteemed reviewer 4 While using a fixed model for feature extraction is a common technique, the concepts of "Dynamic Features" and "Static Features" in this context are unclear. Please provide a clear definition of what you consider dynamic and static features and how they relate to your proposed method. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These features are sensitive to image patterns and structures, which can be crucial for differentiating between different types of lung abnormalities. By combining deep features and GLCM features, it is possible to create a more robust and discriminative feature space for multi-class COVID-19 classification. 5 The baseline methods compared in this paper appear to be outdated, with references [61]-[63] being papers published in 2020. Given the rapid advancements in this field, it would be beneficial to compare your work with more recent publications. For example, consider including a comparison with the work by Pratiwi et al. (2021), "Effect of CT scan image resizing, enhancement and normalization on accuracy of covid-19 detection," which also utilizes VGG16. Providing a justification for not comparing with more recent works would strengthen your paper. The primary outcome measured in this study [73] is the accuracy of COVID-19 detection using CT-scan images and various preprocessing methods. The main findings of this study are that different preprocessing methods, including resizing, enhancement, and normalization, had an impact on the accuracy of COVID-19 classification using a deep learning model (VGG-16), and the highest accuracy of 88.54% was achieved using a combination of deformed resizing, CLAHE enhancement, and normalization to the range of [0 1] and [-1 1]. Initial model comparisons were conducted using contemporary models available at the time of results generation. However, we acknowledge the value of the reviewers' suggestions to benchmark against the most recent state-of-the-art research. 6 Lack of ablation studies. The paper would benefit from the inclusion of ablation studies to demonstrate the impact of each module in the proposed framework. As per suggestion we calculate the results through VGG16 as a single module. a) b) c) d) Fig. 5 Multi-class Covid-19 detection using VGG-16, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 5 presents the multi-class COVID-19 detection results exclusively based on VGG-16 deep features, as visualized through confusion matrices (5a), classification reports (5b), AUC curves (5c), and accuracy loss curves (5d). Relying solely on deep features, the model achieved an overall accuracy of 93% in classifying the four target classes. Notably, the AUC for multi-class differentiation (bacterial, COVID-19, viral) was 0.99, while perfect discrimination (AUC of 1.00) was observed for the normal class. As per suggested, we obtain the results through XGBoost also, the results are presented follow: - a) b) c) Fig. 6 Multi-class Covid-19 detection using XGBoost, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 6 illustrates the multi-class COVID-19 detection performance solely based on XGBoost-processed static features, as depicted in the confusion matrix (6a), classification report (6b), and AUC curve (6c). Relying exclusively on static features, the model achieved an overall accuracy of 86% in classifying the four target classes. 7 The novelty of the proposed method is not clearly evident. The combination of data augmentation, using a fixed CNN model for feature extraction, and training an SVM or XGBoost classifier is a well-established and widely used technique across various fields. To enhance the contribution of this paper, consider highlighting the specific innovations or improvements your method offers compared to existing approaches. Clearly articulate how your proposed framework advances the state-of-the-art or addresses limitations of previous methods in the context of your specific application. This study enhances multiclass COVID-19 prediction through a novel approach encompassing the following key elements: • Optimized pre-processing: Chest X-ray image quality was improved using techniques such as interpolation, data cleaning, augmentation, feature engineering, image enhancement, morphological operations, segmentation, and transformation. • Feature extraction: Dynamic VGG-19 and static GLCM features were computed from multiclass data to capture diverse image characteristics. • Feature selection: A hybrid feature space (HFS) was refined using feature selection methods to eliminate redundant features, thereby improving prediction performance and model size for efficient deployment on edge devices • The optimal HFS was then utilized to the robust optimized XGBoost algorithm for improved prediction • Hyperparameter tuning: The hyperparameters of the XGBoost machine learning algorithm were meticulously optimized. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These features are sensitive to image patterns and structures, which can be crucial for differentiating between different types of lung abnormalities. By combining deep features and GLCM features, it is possible to create a more robust and discriminative feature space for multi-class COVID-19 classification. The hybrid feature space (HFS) • Deep features and GLCM features capture different aspects of image information, leading to improved classification performance. • The combination of these features can better differentiate between subtle visual patterns associated with different lung diseases. • The use of multiple feature types can help to reduce the impact of noise and variations in image quality. By effectively fusing these features and employing a Attachments Attachment Submitted filename: Final Rebuttle letter Aug 08 2024.docx https://doi.org/10.1371/journal.pone.0310011.r004
14 Aug 2024 Decision Letter - Catalin Buiu, Editor PONE-D-24-21569R2Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection TechniquePLOS ONE Dear Dr. Hussain, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Sep 28 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Catalin Buiu Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments: You did not respond to the first reviewer's fifth comment: 5) how many parameters are trained for the VGG model and how the hyperparameters are selected? add in the manuscript. [Note: HTML markup is below. Please do not edit.] [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0310011.r005
Revision 3
16 Aug 2024 Author Response To: Prof. Dr. Joerg Heber PLOS ONE, Editor-in-Chief Re: Manuscript submission to PLOS ONE PONE-D-24-21569R1 Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection Technique Date: August 08, 2024 Dear Dr. Heber We are pleased to inform you that we have revised the manuscript in the light of reviewers’ comments. The reviewers’ recommendations were extremely useful, and we have addressed all their recommendations in the revised manuscript. Please see below for responses to each individual comment. We hope that the revisions in the manuscript and our accompanying responses will be sufficient to make our manuscript suitable for publication in PLOS ONE. Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Response: We have checked each reference and ensured that there is retracted article found. Additional Editor Comments: You did not respond to comment no. 5 from Reviewer 1. Please address it, but in a separate paragraph. Your previous responses were appended to the original comments, making them difficult to follow. Response: We have addressed the comments accordingly. Reviewers’ comments S. No. Comments Rebuttal Reviewer 1 Comments 1 This research, by virtue of the fact, stress the existence and severity of dire results associated with delayed and improper diagnosis which can be a real threat to the survival of humankind. The latest studies have revealed that deep learning models can be used for coronavirus image classification. - add the following work for this statement such as: A Deep learning-based X-Ray Imaging Diagnosis System for Classification of Tuberculosis, COVID19, and Pneumonia Traits using Evolutionary Algorithm The authors [15] proposed a novel automated framework for the classification of tuberculosis, COVID-19, and pneumonia from chest x-ray images using deep learning and an improved optimization technique. The proposed deep learning-based framework achieved high classification accuracy 98.2%, 99.0%, and 98.7%) on three different datasets for tuberculosis, COVID-19, and pneumonia detection from chest X-ray images. The authors employed the Wilcoxon signed-rank test to statistically validate the superior performance of their proposed method. The integration of feature fusion was instrumental in enhancing the method's accuracy. 2 While deep learning models have shown promise in classifying COVID-19 images, there are still limitations that need to be addressed. One such limitation is the lack of research on applying domain adaptation techniques to overcome the challenge of the cross-dataset problem. - add the following work for this statement such as: Deep learning network selection and optimized information fusion for enhanced COVID-19 detection The researchers [16] proposed a wrapper-based technique to improve the classification performance of chest infection (including COVID-19) detection using X-rays by extracting deep features using pretrained deep learning models and optimizing them using various optimization techniques, while also using a network selection technique to select the deep learning models. The proposed deep learning framework achieved a high classification accuracy of 97.7% in detecting chest infections, including COVID-19. Rigorous validation confirmed the framework's reliability for classifying both COVID-19 and other chest infections, suggesting its potential as a valuable tool for clinicians. 3 Related work of this manuscript can be further enhanced by adding few recent works. Also, discuss the cutting edges and gaps that should be linked with the proposed work. The issues have been addressed. Some related work from recent studies as suggested by the reviewers have been incorporated in the literature and discussion sections. Moreover, future directions also been added. 4 How many features are extracted from the VGG model? How many layers used for the re-training on the selected datasets? The VGG16 model was employed for feature extraction, generating 1024-dimensional feature vectors for each image in the dataset. To adapt the model to the specific characteristics of our target problem, the final four fully connected layers of VGG16 were re-trained on the selected datasets. 5 How many parameters are trained for the VGG model and how the hyperparameters are selected? add in the manuscript. Total trainable parameters for VGG-16 were 1,051,648 (first Dense) + 2,099,328 (second Dense) + 4,100 (final Dense) = 3,155,076 trainable parameters. To adapt the model to the specific task, the final four layers were fine-tuned using the selected datasets. Optimal performance hinges on careful selection of hyperparameters and we chosen the learning rate, optimizer, batch size, epochs, and regularization techniques by optimizing the hyperparameters using Bayesian optimization to fine-tune these hyperparameters. 6 What is the purpose of GLCM features and how many features are extracted? The Gray Level Co-occurrence Matrix (GLCM) is a statistical technique used to extract texture features from images by analyzing the spatial relationships between pixel intensities. Its applications span various domains, including SAR imagery for land cover classification (water, vegetation, urban areas) [48] and medical imaging for detecting retinal abnormalities [49], where color features have shown superior accuracy. Gray Level Co-occurrence Matrix (GLCM) analysis computes the frequency of pixel pairs with specific intensity values and spatial relationships, forming a matrix from which statistical features can be extracted. In this study, 25 GLCM features were initially calculated and subsequently reduced to 17 through MeanDropout feature selection. . 7 What is the loss function of the XGBoost classifier and what is the purpose of this classifier? XGBoost traditionally uses convex loss functions, recent research has explored custom and non-convex loss functions to enhance performance in specific applications [59]. For instance, [60] investigated the use of squared logistics loss (SqLL) to improve accuracy. [59] developed weighted softmax loss functions for industrial applications, while [61] proposed a generalized XGBoost method accommodating both convex and some non-convex loss functions. These advancements demonstrate XGBoost's versatility and potential for tailored solutions in various domains, including big data analysis and multi-objective parameter regularization. The purpose of the XGBoost classifier is multifaceted and versatile, as evidenced by various research studies. XGBoost is utilized for enhancing prediction accuracy in diverse fields such as meteorology for hailstorm forecasting [62], detecting patterns in financial datasets to differentiate between solvable and bankrupt situations [63], improving learner performance prediction in Intelligent Tutoring Systems by enhancing models like Performance Factor Analysis and DAS3H [64], and detecting malware in Internet of Medical Things (IoMT) data for better medical assistance through dimensionality reduction and efficient classification [65]. The XGBoost algorithm's scalability, robustness, and proficiency with complex datasets make it a valuable tool for increasing prediction accuracy, addressing class imbalances, enhancing performance prediction models, and improving data analysis in various domains. Reviewer 2 Comments 1 Review the paper for grammatical errors and ensure subject-verb agreement and correct tense usage throughout the manuscript. The issue has been addressed 2 Ensure that all terms and concepts are clearly defined and consistently used throughout the paper. Consider adding a table of abbreviations to improve comprehensibility and avoid ambiguity. The issue has been addressed and highlighted in red color 3 Be consistent when reporting results. Use the same format for all metrics and comparisons. For example, in Table 5, ensure that the number of samples in each category is presented consistently across all discussed models. The issue has been addressed and highlighted in red color 4 Outline potential future work, including improvements to the model and adaptation to other imaging modalities. The issue has been addressed and highlighted in red color in the introduction section Reviewer 3 Comments 1 The paper's formatting needs improvement for consistency. Please ensure that the table formatting is uniform throughout the document. Additionally, standardize the captions for figures and tables to maintain a cohesive style. Lastly, align the equations consistently, either centering them or left aligning them. The issue has been addressed 2 The comparison experiments should be run multiple times to show the mean and std of the results. Fig. 10. Fold vs accuracy curve to distinguish multi-class using ESN-MDFS Figure 10 illustrates the accuracy of the ESN-MDFS model across seven cross-validation folds. The model achieved a mean accuracy of 95.57% with a standard deviation of 0.54, demonstrating consistent performance across different data subsets. 3 Considering that VGG16 is a well-known model, it may not be necessary to include the entire model architecture as a figure or provide extensive introductions to it. Consider simplifying this section to focus on the more novel aspects of your work. The issue has been addressed and details have been incorporated according as advised by the esteemed reviewer 4 While using a fixed model for feature extraction is a common technique, the concepts of "Dynamic Features" and "Static Features" in this context are unclear. Please provide a clear definition of what you consider dynamic and static features and how they relate to your proposed method. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These features are sensitive to image patterns and structures, which can be crucial for differentiating between different types of lung abnormalities. By combining deep features and GLCM features, it is possible to create a more robust and discriminative feature space for multi-class COVID-19 classification. 5 The baseline methods compared in this paper appear to be outdated, with references [61]-[63] being papers published in 2020. Given the rapid advancements in this field, it would be beneficial to compare your work with more recent publications. For example, consider including a comparison with the work by Pratiwi et al. (2021), "Effect of CT scan image resizing, enhancement and normalization on accuracy of covid-19 detection," which also utilizes VGG16. Providing a justification for not comparing with more recent works would strengthen your paper. The primary outcome measured in this study [73] is the accuracy of COVID-19 detection using CT-scan images and various preprocessing methods. The main findings of this study are that different preprocessing methods, including resizing, enhancement, and normalization, had an impact on the accuracy of COVID-19 classification using a deep learning model (VGG-16), and the highest accuracy of 88.54% was achieved using a combination of deformed resizing, CLAHE enhancement, and normalization to the range of [0 1] and [-1 1]. Initial model comparisons were conducted using contemporary models available at the time of results generation. However, we acknowledge the value of the reviewers' suggestions to benchmark against the most recent state-of-the-art research. 6 Lack of ablation studies. The paper would benefit from the inclusion of ablation studies to demonstrate the impact of each module in the proposed framework. As per suggestion we calculate the results through VGG16 as a single module. a) b) c) d) Fig. 5 Multi-class Covid-19 detection using VGG-16, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 5 presents the multi-class COVID-19 detection results exclusively based on VGG-16 deep features, as visualized through confusion matrices (5a), classification reports (5b), AUC curves (5c), and accuracy loss curves (5d). Relying solely on deep features, the model achieved an overall accuracy of 93% in classifying the four target classes. Notably, the AUC for multi-class differentiation (bacterial, COVID-19, viral) was 0.99, while perfect discrimination (AUC of 1.00) was observed for the normal class. As per suggested, we obtain the results through XGBoost also, the results are presented follow: - a) b) c) Fig. 6 Multi-class Covid-19 detection using XGBoost, a) Confusion Matrix, b) Classification Report, c) AUC, d) Accuracy-Loss Curve Figure 6 illustrates the multi-class COVID-19 detection performance solely based on XGBoost-processed static features, as depicted in the confusion matrix (6a), classification report (6b), and AUC curve (6c). Relying exclusively on static features, the model achieved an overall accuracy of 86% in classifying the four target classes. 7 The novelty of the proposed method is not clearly evident. The combination of data augmentation, using a fixed CNN model for feature extraction, and training an SVM or XGBoost classifier is a well-established and widely used technique across various fields. To enhance the contribution of this paper, consider highlighting the specific innovations or improvements your method offers compared to existing approaches. Clearly articulate how your proposed framework advances the state-of-the-art or addresses limitations of previous methods in the context of your specific application. This study enhances multiclass COVID-19 prediction through a novel approach encompassing the following key elements: • Optimized pre-processing: Chest X-ray image quality was improved using techniques such as interpolation, data cleaning, augmentation, feature engineering, image enhancement, morphological operations, segmentation, and transformation. • Feature extraction: Dynamic VGG-19 and static GLCM features were computed from multiclass data to capture diverse image characteristics. • Feature selection: A hybrid feature space (HFS) was refined using feature selection methods to eliminate redundant features, thereby improving prediction performance and model size for efficient deployment on edge devices • The optimal HFS was then utilized to the robust optimized XGBoost algorithm for improved prediction • Hyperparameter tuning: The hyperparameters of the XGBoost machine learning algorithm were meticulously optimized. Deep features extracted from VGG-19 provide a powerful representation of image content. They capture high-level semantic information about the image, such as the presence of specific objects or patterns. In the context of COVID-19 classification, these features can effectively discriminate between different lung pathologies, including pneumonia, viral pneumonia, and COVID-19. By leveraging the hierarchical structure of VGG-19, these features can capture subtle visual patterns that are often challenging for traditional image processing techniques. Static GLCM features, on the other hand, provide complementary information about the texture and spatial relationships between pixels in an image. These Attachments Attachment Submitted filename: Final Rebuttle letter Aug 16 2024.docx https://doi.org/10.1371/journal.pone.0310011.r006
23 Aug 2024 Decision Letter - Catalin Buiu, Editor Enhancing Multiclass COVID-19 Prediction with ESN-MDFS: Extreme Smart Network using Mean Dropout Feature Selection Technique PONE-D-24-21569R3 Dear Dr. Hussain, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Catalin Buiu Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0310011.r007
Formally Accepted
11 Sep 2024 Acceptance Letter - Catalin Buiu, Editor PONE-D-24-21569R3 PLOS ONE Dear Dr. Hussain, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Catalin Buiu Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0310011.r008

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .