Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

Siti Salbiah Samsudin; Hamzah Arof; Sulaiman Wadi Harun; Ainuddin Wahid Abdul Wahab; Mohd Yamani Idna Idris

doi:10.1371/journal.pone.0274896

Peer Review History

Original SubmissionJune 3, 2022
24 Jun 2022 Decision Letter - Jyotismita Chaki, Editor PONE-D-22-16059Skin Lesion Classification Using Multi-Resolution Empirical Mode Decomposition and Local Binary PatternPLOS ONE Dear Dr. Arof, 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 Aug 08 2022 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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Any potentially identifying patient information must be fully anonymized. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter. Additional Editor Comments: Based on the reviewer comments I am suggesting you to revise the manuscript and resubmit. [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: No ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: No ****** 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: 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 ****** 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: Comments: 1. The used dataset is very small, it contains only 70 images of each class for training. Would that be enough to justify the results? 2. It is understandable that the authors used a reduced dataset from HAM10000 data for training to balance the classes. However, the testing could have been done on all the available data. Why have they chosen only 315 images for testing? 3. The Novelty/contribution of the paper is not clear. The achieved accuracy is very good but what lead to the improved accuracy, as compared to other papers. Was it the feature extraction, segmentation, or classification method? 4. Figure 1 is not readable. The resolution needs significant improvements. 5. The Resolution of all figures needs improvement. 6. The literature review is weak, Authors should include more recent relevant publications and compare their results with the results of recent publications. For example, these papers should be included i. Saeed, J. and Zeebaree, S., 2021. Skin lesion classification based on deep convolutional neural network architectures. Journal of Applied Science and Technology Trends, 2(01), pp.41-51. ii. Javaid, Arslan, et al. "Skin Cancer Classification Using Image Processing and Machine Learning." 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). IEEE, 2021. iii. Khan, M.A., Muhammad, K., Sharif, M., Akram, T. and de Albuquerque, V.H.C., 2021. Multi-class skin lesion detection and classification via teledermatology. IEEE journal of biomedical and health informatics, 25(12), pp.4267-4275. iv. Anand, V., Gupta, S., Koundal, D., Nayak, S.R., Nayak, J. and Vimal, S., 2022. Multi-class Skin Disease Classification Using Transfer Learning Model. International Journal on Artificial Intelligence Tools, 31(02), p.2250029. Reviewer #2: The paper provides nothing new, all proposed methods and data are found in the literature. Moreover, the extracted features are not new and they are too much for the dataset. I think authors should combine these methods with deep learning methods rather than conventional machine learning only. ****** 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: Yes: Ali Mohammad Alqudah ******** [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.0274896.r001
Revision 1
13 Jul 2022 Author Response Response to reviewer Reviewer #1: Comments: 1. The used dataset is very small, it contains only 70 images of each class for training. Would that be enough to justify the results? - The data is sufficient since we are using conventional classifiers rather than CNN with deep learning. Therefore, we believe the results are significant. 2. It is understandable that the authors used a reduced dataset from HAM10000 data for training to balance the classes. However, the testing could have been done on all the available data. Why have they chosen only 315 images for testing? - There are many researchers who choose to separate the training and test images. In fact, we hope that separating the training and test images would allow us to see whether the ANN manages to learn the underlying pattern of each class of the training data and then generalize it to the test data that it has not seen before. 3. The Novelty/contribution of the paper is not clear. The achieved accuracy is very good but what lead to the improved accuracy, as compared to other papers. Was it the feature extraction, segmentation, or classification method? - The novelty comes from using features from the MREMD in addition to features from the traditional LBP to help improve the accuracy. On top of that we also compare the performances of a few different classifiers 4. Figure 1 is not readable. The resolution needs significant improvements. - Corrected as suggested. 5. The Resolution of all figures needs improvement. - Improved as suggested 6. The literature review is weak, Authors should include more recent relevant publications and compare their results with the results of recent publications. For example, these papers should be included i. Saeed, J. and Zeebaree, S., 2021. Skin lesion classification based on deep convolutional neural network architectures. Journal of Applied Science and Technology Trends, 2(01), pp.41-51. ii. Javaid, Arslan, et al. "Skin Cancer Classification Using Image Processing and Machine Learning." 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). IEEE, 2021. iii. Khan, M.A., Muhammad, K., Sharif, M., Akram, T. and de Albuquerque, V.H.C., 2021. Multi-class skin lesion detection and classification via teledermatology. IEEE journal of biomedical and health informatics, 25(12), pp.4267-4275. iv. Anand, V., Gupta, S., Koundal, D., Nayak, S.R., Nayak, J. and Vimal, S., 2022. Multi-class Skin Disease Classification Using Transfer Learning Model. International Journal on Artificial Intelligence Tools, 31(02), p.2250029. - Added as recommended Reviewer #2: The paper provides nothing new, all proposed methods and data are found in the literature. Moreover, the extracted features are not new and they are too much for the dataset. I think authors should combine these methods with deep learning methods rather than conventional machine learning only. - Since the data are limited, it is not suitable to use deep learning for classification since it requires a lot of data to properly train. Furthermore, the classification rates achieved by simple conventional classifiers are high that resorting to deep learning is unnecessary. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0274896.r002
7 Sep 2022 Decision Letter - Jyotismita Chaki, Editor Skin Lesion Classification Using Multi-Resolution Empirical Mode Decomposition and Local Binary Pattern PONE-D-22-16059R1 Dear Dr. Arof, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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, Jyotismita Chaki, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): I am happy to inform you that reviewers are satisfied with the revised version of the manuscript. Therefore I am provisionally accepting the manuscript for publication. Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. 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 ******** 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 ****** 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 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 ****** 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 ****** 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: The authors have addressed all my concerns and I do not have any further objections. The quality of the paper is improved significantly, Therefore, I recommend its acceptance for publication in PLOS ONE. ****** 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 ******** https://doi.org/10.1371/journal.pone.0274896.r003
Formally Accepted
12 Sep 2022 Acceptance Letter - Jyotismita Chaki, Editor PONE-D-22-16059R1 Skin Lesion Classification Using Multi-Resolution Empirical Mode Decomposition and Local Binary Pattern. Dear Dr. Arof: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Jyotismita Chaki Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0274896.r004

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