Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection and segmentation

Prathima Devadas; Gandhi Mathivanan

doi:10.1371/journal.pone.0342185

Peer Review History

Original SubmissionJuly 21, 2025
16 Oct 2025 Decision Letter - Subramani Neelakandan, Editor Dear Dr. Devadas, 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 Nov 30 2025 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. 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, Subramani Neelakandan Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. 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, we expect all author-generated code to 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.PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. 4. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Additional Editor Comments (if provided): Author presented the research entitled “Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection” well. Here I would like to point out some suggestion to proceed further. The manuscript should specify dataset sources (e.g., Figshare, Br35H, BraTS), image count, resolution, and data partition ratios (training/testing). Clarify preprocessing techniques—normalization, augmentation, and noise reduction—to ensure reproducibility. The paper mentions radiomics and GLCM integration, but the fusion mechanism (concatenation, attention weighting, or dimensionality reduction) is not clearly explained. Add an ablation study to show the contribution of each component (Mask R-CNN, radiomics, GLCM). Ensure consistency in terminology—use either “brain tumor classification” or “detection and segmentation” throughout the manuscript. Add recent literature references (2022–2025) comparing hybrid deep learning and radiomics models for medical imaging. Include a “Limitations and Future Work” section discussing scalability, dataset diversity, and model generalizability. [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? Reviewer #1: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: The manuscript proposes a new technique called “Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection”. This is a research project with scientific and practical significance. The manuscript is presented with a reasonable layout and rich simulation results, demonstrating the effectiveness of the proposed method. To improve the quality of the manuscript, the following ideas should be considered for editing. Review Comments: 1. Need to change the “the offering of the our work are follows” in the introduction section. 2. Abbreviations only need explanation the first time they are used. There are a number of abbreviations that are explained multiple times in the manuscript. For example, CNN is explained twice in the literature. 3. Limited literature review has been carried out, so limitations of existing works are not reflected. 4. How does the R2CNN architecture improve tumor region localization compared to standard CNN models? 5. Many of the equations are part of the related sentences. Attention is needed for correct sentence formation. 6. What role do radiomics features play in enhancing the detection accuracy of brain tumors? 7. The manuscript does not discuss potential limitations such as scalability, real-world applicability, or robustness to unseen data. 8. Is the proposed method computationally efficient enough for real-time or clinical application? 9. Rectify the minor errors observed in the paper through thorough copy editing. Improve the writing style, English language, and grammar. 10. Need to improve the conclusion section. ****** 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 ******** [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.0342185.r001
Revision 1
24 Nov 2025 Author Response Additional Editor Comments (if provided): Author presented the research entitled “Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection” well. Here I would like to point out some suggestion to proceed further. The manuscript should specify dataset sources (e.g., Figshare, Br35H, BraTS), image count, resolution, and data partition ratios (training/testing). Response: Thank you for your comment. The manuscript has been updated to clearly specify the dataset sources (Figshare and Harvard Whole Brain Atlas), including image count, resolution, and data partition ratios for training, validation, and testing. Clarify pre-processing techniques—normalization, augmentation, and noise reduction—to ensure reproducibility. Response: Thanks for your comment. We have added the pre-processing techniques section, which now includes details on normalization, augmentation, and noise reduction. These steps are critical for ensuring reproducibility across experiments. The paper mentions radiomics and GLCM integration, but the fusion mechanism (concatenation, attention weighting, or dimensionality reduction) is not clearly explained. Response: Thanks for your comment. We have added the Feature Fusion Mechanism section Add an ablation study to show the contribution of each component (Mask R-CNN, radiomics, GLCM). Ensure consistency in terminology—use either “brain tumor classification” or “detection and segmentation” throughout the manuscript. Response: Thanks for your comment. We have added an ablation study to demonstrate the contribution of each component—Mask R-CNN, radiomics, and GLCM—to the overall model performance. The corresponding results and discussion have been included in the revised manuscript. Additionally, we have ensured consistency in terminology throughout the manuscript by uniformly using the term “brain tumor detection and segmentation. Add recent literature references (2022–2025) comparing hybrid deep learning and radiomics models for medical imaging. Response: Thank you for your insightful comment. Recent 2022–2025 references added. Include a “Limitations and Future Work” section discussing scalability, dataset diversity, and model generalizability Response: Thank you for your valuable comment. A section on Limitations and Future Work has been added after the Conclusion. This section addresses the challenges related to scalability, dataset diversity, and the generalizability of the model, and outlines potential directions for future research. 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 ________________________________________ 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: 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: Yes ________________________________________ 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 ________________________________________ 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: The manuscript proposes a new technique called “Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection”. This is a research project with scientific and practical significance. The manuscript is presented with a reasonable layout and rich simulation results, demonstrating the effectiveness of the proposed method. To improve the quality of the manuscript, the following ideas should be considered for editing. Review Comments: 1. Need to change the “the offering of the our work are follows” in the introduction section. Response: Thank you for your comment. We have revised the phrase “the offering of our work are follows” to “the contributions of our work are as follows” in the Introduction section. 2. Abbreviations only need explanation the first time they are used. There are a number of abbreviations that are explained multiple times in the manuscript. For example, CNN is explained twice in the literature. Response: Thank you for your comment. We have carefully reviewed the manuscript and ensured that each abbreviation is defined only at its first occurrence (e.g., CNN, MRI, MLP). Repeated explanations have been removed for consistency and clarity. 3. Limited literature review has been carried out, so limitations of existing works are not reflected. Response: Thank you for your comment. We have added the Critical Review and Observed Limitations of Prior Studies and corrected limitations of exiting model. 4. How does the R2CNN architecture improve tumor region localization compared to standard CNN models? Response: Thank you for your comment. We have added the content in section Improvement over Standard CNN Architectures and Localization Advantage of Mask R-CNN (R2CNN). 5. Many of the equations are part of the related sentences. Attention is needed for correct sentence formation. Response: Thank you for your comment. We have carefully reviewed the manuscript and revised the related sentences to ensure that all equations are correctly integrated within their corresponding textual context for improved readability and grammatical accuracy. 6. What role do radiomics features play in enhancing the detection accuracy of brain tumors? Response: Thank you for your comment. Radiomics features quantitatively capture tumor texture, shape, and intensity variations, enhancing the model’s ability to identify subtle heterogeneity and improving the accuracy of brain tumor detection. 7. The manuscript does not discuss potential limitations such as scalability, real-world applicability, or robustness to unseen data. Response: Thank you for your comment. We appreciate this valuable observation. The revised manuscript now includes a dedicated section titled “Limitations and Future Work”, which explicitly discusses constraints related to scalability, real-world deployment, computational complexity, and generalization to unseen datasets. This section also outlines strategies for enhancing robustness and cross-domain applicability in future research 8. Is the proposed method computationally efficient enough for real-time or clinical application? Response: Thank you for your comment. The proposed method demonstrates computational efficiency suitable for near–real-time clinical application, with inference times of approximately 1.563 s (non-tumor) and 3.175 s (tumor), confirming its feasibility for clinical use. We have added the answer in the Results and Discussion section, following the Processing Time Analysis (Table 2 and Fig. 8) subsection. 9. Rectify the minor errors observed in the paper through thorough copy editing. Improve the writing style, English language, and grammar. Response: Thank you for your comment. We have corrected the grammatical in the manuscript. 10. Need to improve the conclusion section. Response: Thank you for your comment. We have improved the conclusion section. Attachments Attachment Submitted filename: Prathima review comments.docx https://doi.org/10.1371/journal.pone.0342185.r002
19 Jan 2026 Decision Letter - Subramani Neelakandan, Editor Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection and segmentation PONE-D-25-34631R1 Dear Dr. Devadas, 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. For questions related to billing, please contact billing support . 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, Subramani Neelakandan Academic Editor PLOS One Additional Editor Comments (optional): Author revised the manuscript based on reviewer comments therefore article will be recommended to consider next process. Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: (No Response) ****** 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 ******** https://doi.org/10.1371/journal.pone.0342185.r003
Formally Accepted
Acceptance Letter - Subramani Neelakandan, Editor PONE-D-25-34631R1 PLOS One Dear Dr. Devadas, 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 You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days 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. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. 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. Subramani Neelakandan Academic Editor PLOS One https://doi.org/10.1371/journal.pone.0342185.r004

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 .