PONE-D-21-02672

Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19

PLOS ONE

Dear Dr. Çallı,

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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: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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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

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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: No

Reviewer #2: Yes

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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: I have a few observations regarding this submission before accept.

Comment #1: How does the proposed system handle missing data? A mathematical proof is needed.

Comment #2: A proof to show the robustness of the architecture is highly needed.

Comment #3: COVID-19 researches from different directions must be cited to properly present the background of the study.

Shah Muhammad Azmat Ullah, Md. Milon Islam, Saifuddin Mahmud, Sheikh Nooruddin, S. M. Taslim Uddin Raju and Md. Rezwanul Haque, “Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic” SN Computer Science, Springer, vol. 2, no.1, pp. 18, 2020.

Md. Milon Islam, Saifuddin Mahmud, L. J. Muhammad, Md. Rabiul Islam, Sheikh Nooruddin and Safial Islam Ayon, “Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19)," SN Computer Science, Springer, vol. 1, no. 6, pp. 320, Sep. 2020.

Md. Milon Islam, Shah Muhammad Azmat Ullah, Saifuddin Mahmud and S. M. Taslim Uddin Raju "Breathing Aid Devices to Support Novel Coronavirus (COVID-19) Infected Patients," SN Computer Science, Springer, vol. 1, no. 5, pp. 274, Aug. 2020.

Mohammad Marufur Rahman, Md. Motaleb Hossen Manik, Md. Milon Islam, Saifuddin Mahmud and Jong-Hoon Kim," An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE, Vancouver, BC, Canada, pp. 1-5, 9-12 Sep., 2020.

Comment #4: Novelty is confusing. A highlight is required. The main contributions of the manuscript are not clear. The main contributions of the ‎article must be very clear and would be better if summarize ‎them into 3-4 points at the ‎end of the introduction.‎

Comment #5: The following references must be cited in Introduction section to describe the deep learning based systems for COVID-19 diagnosis.

M. M. Islam, F. Karray, R. Alhajj and J. Zeng, "A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)," in IEEE Access, doi: 10.1109/ACCESS.2021.3058537.

Amanullah Asraf, Md. Zabirul Islam, Md. Rezwanul Haque and Md. Milon Islam," Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic," SN Computer Science, Springer, vol. 1, no. 6, pp. 363, Nov. 2020.

Muhammad Lawan Jibril, Md. Milon Islam, Usman Sani Sharif and Safial Islam Ayon, “Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients Recovery,” SN Computer Science, Springer, vol. 1, no. 4, pp. 206, Jun. 2020.

Comment #6: Methodology is not clear. Provide an algorithm and flowchart of the whole work. The authors need to add a new figure to show the main structure of the proposed system. ‎This will help the reader to get a better understanding of what is going on in the proposed ‎system.‎

Comment #7: Comparison with the following works are highly required.

Md. Zabirul Islam, Md. Milon Islam and Amanullah Asraf, "A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images," Informatics in Medicine Unlocked, Elsevier, vol. 20, pp. 100412, Aug. 2020.

Prottoy Saha, Muhammad Sheikh Sadi and Md. Milon Islam," EMCNet: Automated COVID-19 Diagnosis from X-ray Images using Convolutional Neural Network and Ensemble of Machine Learning Classifiers," Informatics in Medicine Unlocked, Elsevier, vol. 22, pp. 100505, Jan. 2021.

M. M. Islam, M. Z. Islam, A. Asraf, and W. Ding, “Diagnosis of COVID-19 from X-rays Using Combined CNN-RNN Architecture with Transfer Learning,” Aug. 2020. [Online]. Available: https://www.medrxiv.org/content/10.1101/2020.08.24.20181339v1.

Comment #8: Abstract is unnecessarily wordy. Make it brief and concise. Also, Conclusion should clearly state the outcome. Some of the obtained results need to be highlighted in the conclusion section.‎

Comment #9: There are lots of typos. English needs to revise again with a professional editing service. Also, the figures are not clear in some cases.

Comment #10: Mention the limitations and future works of the developed system elaborately.

Comment #11: Please, add a small paragraph to describe the main structure of the manuscript at the end ‎of the introduction.‎

Comment #12: All the figures presented in the experimental results section are not discussed by the ‎authors. This is a critical issue in this section.‎

Comment #13: 10-fold cross-validation is adopted, right? Authors should state this in the main text explicitly.

Comment # 14: Experiments with benchmark dataset (J. P. Cohen, P. Morrison, and L. Dao, “COVID-19 Image Data Collection,” 2020, arXiv: 2003.1159. [Online]. Available: https://arxiv.org/abs/2003.11597) are highly needed to proof the universal use cases.

Reviewer #2: the Subject was interesting and I prefer to publish it but there is a minor Revision that I am not convinced about the performance of the models. the authors don't mention anything about Overfitting the model. the ROC chart doesn't show in the manuscript and it's an important problem. In table 3 (Comparison of models on the test dataset) it's better to show the models chart to compare their AUC and another metric. it's conventional to show details of confusion matrix metrics including(Sensitivity and Specificity ).

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Reviewer #1: Yes: Md. Milon Islam

Reviewer #2: Yes: Mustafa Ghaderzadeh

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Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19

PONE-D-21-02672R1

Dear Dr. Çallı,

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.

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Kind regards,

Ruxandra Stoean

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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: (No Response)

Reviewer #2: All comments have been addressed

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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: No

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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: No

Reviewer #2: Yes

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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: No

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: The authors did not address all of my previous comments clearly. Even they have skipped some comments. The response is in shallow form that violate the rule for high quality journal. Actually, the revision in it's current form do not hold the criteria for publication. Another round of review of my previous comments is highly required to get the manuscript in publishable format.

Reviewer #2: this study is worthwhile and it can be published ,

the subject is interesting and applicable.

the can be better but in current form and format is acceptable and can be published in PLOSE ONE

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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: Yes: Mustafa Ghaderzadeh