Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques

Hilman Nordin; Bushroa Abdul Razak; Norrima Mokhtar; Mohd Fadzil Jamaludin; Adeel Mehmood

doi:10.1371/journal.pone.0316996

Peer Review History

Original SubmissionJuly 17, 2024
17 Sep 2024 Decision Letter - Yile Chen, Editor PONE-D-24-29724Automated Mold Defects Classification in Paintings: A Comparison of Machine Learning and Rule-Based TechniquesPLOS ONE Dear Dr. Mokhtar, 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 01 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. 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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. [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. 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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: 1. In Introduction, the last paragraph citations must be revised carefully. 2. In Related Works, I expected to see a comparison between previous models (techniques) according to advantages and disadvantages as minimum. 3. In Methodology, the first paragraph shouldn't be about the dataset. But it should be about the proposed model. 4. In Methodology, Table 1 caption should be rephrased. 5. In Results and Discussion, Table 2 must be sorted according to the most important evaluation metric/s. 6. A comparison between previous and proposed techniques should be considered in Results and Discussion section. 7. The small size of the dataset used may affect the accuracy of models. This must be considered in the future work. Reviewer #2: Thank you for the opportunity to review this manuscript. The study presents a comparative analysis of rule-based morphological filtering and machine learning techniques for detecting and classifying mold defects in fine art paintings. The methodology is well-described and the results demonstrate the potential of these techniques for improving the accuracy and precision of mold defect detection in art conservation. 1. The statistical analysis, while generally sound, could be more rigorous. The authors should consider adding significance tests to compare the performance of the different methods and providing confidence intervals for the performance estimates. Hyperparameter tuning for the machine learning models could also be explored. 2. There are some minor typographical and grammatical errors throughout the manuscript that should be corrected in a revision. Thank you. ****** 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: Yes: Tamer Abdel Latif Ali 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.0316996.r001
Revision 1
4 Dec 2024 Author Response Reviewer 1 Comments • Comment 1: In Introduction, the last paragraph citations must be revised carefully. • Response 1: Thank you for the feedback. The last paragraph in the introduction containing the citations has been revised for clarity (see line 73). A concluding remark has also been added at the end of the paragraph. • Comment 2: In Related Works, I expected to see a comparison between previous models (techniques) according to advantages and disadvantages as minimum. • Response 2: Thank you for the feedback. Two paragraphs were added to explain the advantages and disadvantages of the techniques discussed in the paper (see line 102). • Comment 3: In Methodology, the first paragraph shouldn't be about the dataset. But it should be about the proposed model. • Response 3: Thank you for the feedback. The first paragraph has been revised to explain about the proposed model rather than the dataset (see line 130). • Comment 4: In Methodology, Table 1 caption should be rephrased. • Response 4: Thank you for your feedback. We have reviewed the caption for Table 1 and made the necessary revisions. The updated caption now reads (see line 172): o “Distribution of Quantitative Mold Features used in the Classification Models” • Comment 5: In Results and Discussion, Table 2 must be sorted according to the most important evaluation metric/s. • Response 5: Thank you for your valuable feedback. We have revised Table 2 to sort results according to the most important evaluation metric, Detection. This provides a clearer and a more focused comparison of the different methods (see line 287). • Comment 6: A comparison between previous and proposed techniques should be considered in Results and Discussion section. • Response 6: We have carefully considered your suggestions and incorporated significance analysis to compare the previous and the proposed techniques in the Results and Discussion section (see line 309). • Comment 7: The small size of the dataset used may affect the accuracy of models. This must be considered in the future work. • Response 7: We appreciate the reviewer’s comment regarding the potential impact of dataset size on model accuracy. We acknowledge that a larger dataset could enhance our results. In future work, we plan to apply data augmentation techniques, explore the use of Convolutional Neural Network, using multiple feature extraction algorithms and addressing the issue of dataset class imbalance (see line 354). Reviewer 2 Comments • Comment 1: The statistical analysis, while generally sound, could be more rigorous. The authors should consider adding significance tests to compare the performance of the different methods and providing confidence intervals for the performance estimates. Hyperparameter tuning for the machine learning models could also be explored • Response 1: o We agree that strengthening the statistical analysis would enhance the robustness of our findings. We have incorporated the use of significance test in the form of paired t-test and reported the p-values to assess the significance of it in Table 3. We also have added another literature to the references to support the approach (see line 309). o While we acknowledge that hyperparameter tuning can potentially improve model performance, we chose to use the machine learning model as it is to establish baseline comparison with rule-based method. In future studies, we plan to explore hyperparameter tuning to optimize our models and potentially achieve better results (see line 366). • Comment 2: There are some minor typographical and grammatical errors throughout the manuscript that should be corrected in a revision. • Response 2: The manuscript has been sent for proofreading and typographical and grammatical errors throughout the manuscript has been addressed. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0316996.r002
20 Dec 2024 Decision Letter - Yile Chen, Editor Automated Mold Defects Classification in Paintings: A Comparison of Machine Learning and Rule-Based Techniques PONE-D-24-29724R1 Dear Dr. Mokhtar, 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. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. 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 ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. 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 ****** 5. 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 ****** 6. 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: (No Response) Reviewer #2: I have reviewed the revised manuscript and am pleased to see that all previous comments have been thoroughly addressed. The addition of statistical analysis, improved organization, and enhanced discussion of limitations and future work have significantly strengthened the paper. ****** 7. 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 ******** https://doi.org/10.1371/journal.pone.0316996.r003
Formally Accepted
14 Jan 2025 Acceptance Letter - Yile Chen, Editor PONE-D-24-29724R1 PLOS ONE Dear Dr. Mehmood, 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. Yile Chen Academic Editor PLOS ONE Attachments Attachment Submitted filename: pone.0316996.docx https://doi.org/10.1371/journal.pone.0316996.r004

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