Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction

Noshaba Qasmi; Rimsha Bibi; Sajid Rashid

doi:10.1371/journal.pone.0313329

Peer Review History

Original SubmissionAugust 23, 2024
11 Sep 2024 Decision Letter - Ramada Rateb Khasawneh, Editor PONE-D-24-36557Conus specie recognition through combinatory action of supervised learning and deep learning-based feature extractionPLOS ONE Dear Dr. Rashid, 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. the article discuses nice work but I have some comments: the article need some English editing also you need to discuss the literature in a depth and compare it to your work and show the novelty of your work finally, please make sure that the text is following PLOSONE criteria Good luck Please submit your revised manuscript by Oct 26 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, Ramada Rateb Khasawneh 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, 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. When completing the data availability statement of the submission form, you indicated that you will make your data available on acceptance. We strongly recommend all authors decide on a data sharing plan before acceptance, as the process can be lengthy and hold up publication timelines. Please note that, though access restrictions are acceptable now, your entire data will need to be made freely accessible if your manuscript is accepted for publication. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If you are unable to adhere to our open data policy, please kindly revise your statement to explain your reasoning and we will seek the editor's input on an exemption. Please be assured that, once you have provided your new statement, the assessment of your exemption will not hold up the peer review process. 4. 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. 5. 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: the article discuses nice work but I have some comments: the article need some English editing also you need to discuss the literature in a depth and compare it to your work and show the novelty of your work [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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 Reviewer #2: 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 Reviewer #2: 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 authors proposed "Conus specie recognition through combinatory action of supervised learning and deep learning-based feature extraction". The structure of the article is well structured. But authors will consider following comments. 1.Proofread the entire manuscript. 2.Draw a Graphical abstract of the proposed approach. 3.Compare your approach with previously available existing approaches. 4.Explain the novelty of the proposed approach. 5.Explain why you choose the deep learning based feature extraction. 6. Gold standard dataset is available freely to Conus specie recognition? 7. What type of features played an important role to recognize Conus specie ? 8.compare your approach with previous traditional feature extraction approaches. Reviewer #2: The manuscript can be accepted. I just would like to suggest you include the conclusion heading and try to provide the figures in as higher quality as you can because of the nature of this work. Also, I recommend authors to include some more models rather just being on very simplest model. ****** 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 ******** [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.0313329.r001
Revision 1
2 Oct 2024 Author Response On behalf of all coauthors, I would like to thank you and both reviewers for the valuable feedback to enhance the quality of our manuscript entitled “Conus specie recognition through combinatory action of supervised learning and deep learning-based feature extraction”. Reviewer #1 The authors proposed "Conus specie recognition through combinatory action of supervised learning and deep learning-based feature extraction". The article is well structured. But authors will consider the following comments. 1. Proofread the entire manuscript. We have thoroughly reviewed the manuscript for any grammatical errors and sentence structure anomalies. All sections have been carefully proofread and issues have been resolved. We believe that the manuscript now meets the standards expected for publication in terms of readability. Some changes according to requirements for PLOS ONE: • Removed the heading numbers, and changed heading 1 to 18, heading 2 to 16, heading 3 to 14. • Changed in-text citation style from [1][2] to [1,2]. Same for all in-text citations. • More literature review information related to random forest and imaging was added to the introduction. • Changed Figure 1 to Fig 1 in both paragraph and caption (did this for all figures). • Unbold the table reference in paragraph. • Changed Figure S1 to S1 Fig (same for supplementary table). • Changed the Fig 6 caption placement right below the paragraph. • Added the reason for choosing the deep-learning model and the novelty of our approach in the discussion section. • Add funding as separately according to the requirement. 2. Draw a Graphical abstract of the proposed approach. We have included a graphical abstract to describe the details of a work plan. 3. Compare your approach with previously available existing approaches. Prior to initiate this work, we have thoroughly overviewed previous approaches. Generally, these methods for Conus specie recognition rely on the traditional image processing techniques or features, which have limitations such as sensitivity to image noise and variability in specie appearance. In this study, we utilized hybrid approach that combines deep learning with traditional feature extraction methods followed by supervised learning, feature extraction automation, improved handling of interspecies variability, and noise resilience. These steps resulted in a more reliable and scalable model as compared to conventional methods. 4. Explain the novelty of the proposed approach. The novelty of our approach lies in the integration of deep learning-based feature extraction with supervised learning. Deep learning captures nuanced details from images, while supervised learning optimizes classification accuracy. This combination allows a more robust and automated system which is capable of efficiently handling specie recognition task adaptable to other biological datasets. 5. Explain why you choose the deep learning based feature extraction. Deep learning, particularly VGG16, is known to automatically extract hierarchical features through images. For Conus specie shells, which exhibit subtle morphological differences, deep learning-based feature extraction captures fine details such as shell patterns and color gradients. This method is more reliable and scalable than manual feature extraction, especially for large datasets. 6. Gold standard dataset is available freely to Conus specie recognition? The dataset was mainly extracted from ConoServer database, which is freely available. 7. What type of features played an important role to recognize Conus species? Critical features for accurate Conus specie classification included shell patterning, color variations, texture, surface details, and overall shell shape and size. 8. Compare approach with previous traditional feature extraction approaches. Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set. In discussion section, we addressed the rational for using deep learning-based feature extraction method and “Haralick” method. Haralick features are derived from the Gray Level Co-occurrence Matrix. This matrix records how many times two gray-level pixels adjacent to each other appear in an image. Then based on this matrix, Haralick proposes 13 values that can be extracted from the Gray Level Co-occurrence Matrix to quantify texture. The traditional feature extraction approaches including Principal Component Analysis, Local Binary Patterns (LBP) and Natural Language Processing (NLP)-based techniques are not well defined as image-derived text data is an important source for NLP-based systems. The objects recognized in images (nouns) can be linked to actions (verbs) and attributes (adjectives) described in text, creating a more comprehensive understanding of a scene. The problems are associated with poor alignment of visual and textual data due to differences in data structure and representation. More sophisticated algorithms are required to cope with these issues. Similarly, we cannot use LBP for cone shell detection as it is sensitive to image noise which hinders its ability to accurately capture texture information. The LBP operator compares neighboring pixel intensities, and if there is noise in the image, it can result in incorrect binary values that can affect the resulting LBP histogram. Reviewer #2 The manuscript can be accepted. I just would like to suggest you include the conclusion heading and try to provide the figures in as higher quality as you can because of the nature of this work. Also, I recommend authors to include some more models rather just being on very simplest model. We are thankful to worthy reviewer for appreciation. As per your suggestion, we have added a "Conclusion" heading to the manuscript to clearly delineate our findings and their implications. Additionally, we have enhanced the quality of the figures to ensure they meet the standards. For image recognition, there are numerous methods which have been proposed recently; however, not all models fit specific tasks due to nature of dataset. There is an issue of overfitting upon dealing with small dataset. The model performs well on the training data but then fails on test data, and lacks performance. The application of advanced neural network structures poses a limitation of their implementation upon architecture variation. Deep learning-based pre-trained models including VGG, ResNet, and Inception are considered as more accurate and efficient in computer vision; however, these models are relatively new and there are certain challenges associated to monitor their benefits. We have carefully addressed the comments raised by worthy reviewers. We appreciate the opportunity to revise the manuscript and resubmit it for reconsideration. Thank you for your valuable time. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0313329.r002
8 Oct 2024 Decision Letter - Ramada Rateb Khasawneh, Editor PONE-D-24-36557R1Conus specie recognition through combinatory action of supervised learning and deep learning-based feature extractionPLOS ONE Dear Dr. Rashid, 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 22 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, Ramada Rateb Khasawneh 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: the article need some English editing can you discuses the literature more ... and compare your finding with other finding [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.0313329.r003
Revision 2
16 Oct 2024 Author Response Editor PLOS ONE Dear Editor, On behalf of all coauthors, I would like to thank you and both reviewers for the valuable feedback to enhance the quality of our manuscript entitled “Recognition of Conus Species Using a Combined Approach of Supervised Learning and Deep Learning-Based Feature Extraction”. 1. Proofread the entire manuscript. We have thoroughly reviewed the manuscript for any grammatical errors and sentence structure anomalies. All sections have been carefully proofread and issues have been resolved. We believe that the manuscript now meets the standards expected for publication in terms of readability. 2. Compare current approach with previous approaches. We have carefully addressed the comments raised by worthy reviewers. We appreciate the opportunity to revise the manuscript and resubmit it for reconsideration. Thank you for your valuable time. Sincerely, Sajid Rashid sajid@qau.edu.pk Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0313329.r004
23 Oct 2024 Decision Letter - Ramada Rateb Khasawneh, Editor Recognition of Conus Species Using a Combined Approach of Supervised Learning and Deep Learning-Based Feature Extraction PONE-D-24-36557R2 Dear Dr. Rashid, 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, Patrick Goymer Staff Editor PLOS ONE on behalf of Ramada Rateb Khasawneh Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0313329.r005
Formally Accepted
29 Oct 2024 Acceptance Letter - Ramada Rateb Khasawneh, Editor PONE-D-24-36557R2 PLOS ONE Dear Dr. Rashid, 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. Ramada Rateb Khasawneh Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0313329.r006

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 .