PONE-D-23-18788COVID-19 Detection and Web Deployment from CT scan and Xray Images Using Deep LearningPLOS ONE

Dear Dr. Mohsin,

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PLOS ONE

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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: Yes

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

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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: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: No

Reviewer #2: No

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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: COVID-19 Detection and Web Deployment from CT scan and Xray Images Using Deep Learning

Typos Errors

“Resnet50:At 50 layers deep and sporting 25.5 million parameters” : “sporting” should it not be “supporting”

Section I Confussion matrix : What is “ In this chapter”

In Figure 11, the y-axis scale is not the same for graphs (a) – (d). This can be confusing. It will be better to have the same scale for all graphs.

In Figure 12 the format for numbers in True Positive and True negative should be changed so that we can easily see the numbers.

Section H – Whole paragraph repeated:

“Our model sends predictions to the flask app. Since it is research work, we ran the service in a local machine instead of deploying online. Figure 18 below shows how to run the predictions of the model in our local environment and Figure19 shows how the webpage looks when predicting.”

Fig 18 has the same screen shot given twice.

Sections requiring additional explanations:

In the data augmentation section, elaborate on “feature wise standardization”.

“From the ROC curves in Figure 13, we can see that the ResNet50 model has low training time”, It is not clear how the curve show low training time. Please explain.

General comments:

As COVID-19 is no longer a pandemic, the work should be reframed to cover a more generalize classification of CT scan and Xray images.

Reviewer #2: The paper 'COVID-19 Detection and Web Deployment from CT Scan and Xray Images

Using Deep Learning' presents the work done and findings of the authors on the detection of covid 19 using Deep Learning. Four different DL models were trained on both x-ray images and CT-scan images.

1. Although the authors have mentioned a number of works in their literature review they have not provided the results obtained in these works and more importantly have not critically analysed existing work compared to their work. Have the authors been able to do better in their proposed solution? or are the results comparable to existing ones? or worse?

2. In the literature section, the datasets that have been selected are already mentioned and detailed – the description of the datasets should be in the experimental details/implementation

3. Reference to the figures in the text currently does not follow the Plos one formatting guidelines and is also inconsistent in the paper. In some cases, Figure 1, Figure 2 are used in the text and in some cases Fig3, Fig4, Fig 5 etc are used.

4. The Python code should not be given in the paper.

5. Table caption should be at the top of the table

6. It is unclear why the numbers in the confusion matrices are given as 1.8e+02

7. Both VGG19 and Xception have given an accuracy of 100% and no explanations were given on this. Were these cases of overfitting? Although the authors have stated that they have used a Flatten layer to flatten their input features and a Dropout layer to overcome overfitting, there seems to be overfitting here.

8. There is no need to explain how to use Flask in the paper

9. The paper is not always written in the format of a journal paper and more of a report in many cases.

10. The authors have used either 100 or 500 epochs but have not explained why they chose these numbers and what the optimum number of epochs is. Also, early stopping could have been looked into.

11. Although the users have mentioned that they have used data augmentation, they have not specified how many images they had after the augmentation step. Moreover, they did not specify whether augmentation was used with the x-ray images or for the CT-scan images.

12. Language to be improved in multiple instances. The sentences are not always clear and it makes for difficult reading of the paper.

(a) Abstract

- ‘On the contrary, current detection methods for the disease are time-consuming and expensive with low accuracy and precision.’- remove on the contrary

- ‘To address such situations, we have designed a framework for COVID-19 detection using multiple deep learning algorithms further accompanied by a deployment scheme, which can become less time consuming and highly accurate comparatively.’ reword the part after which eg which aims at reducing the time to detect the disease and errors.

- Performance achieved not mentioned in the abstract

(b) CT-scan should be used consistently in the paper, Ct-scan has also been used in some cases.

(c) D.Datasets

- CT-Scan images - ‘During the first of January [20] 2021, the dataset consisted’ should be reworded, the dataset consisted of the number of images either on the first of January or during the month of January.

- The sentence ‘The appropriateness of this dataset has been performed by a senior radiologist in Tongji Hospital, Wuhan, China, who has been assigned in diagnosis and

conducting of a bigger number of covid19 patients in the eve of the prevalence of this disease between January and April [25].’ is not clear and should be reworded.

(d) Inception v3

- The following sentence is unclear ‘Inception V3 CNN base deep neural network with 48 layers of module and can convolute 1ⅹ13ⅹ3 and 5ⅹ5 convolution.‘

- 1ⅹ13ⅹ3 and 5ⅹ5 convolution – comma to be added

- The formatting to be looked into for this section

(e) Xception

- The following sentence is unclear ‘Xception was 71 layers deep and had 23 million parameters’

(f) And many others – the English should be reviewed

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Reviewer #1: Yes: Sunilduth Baichoo

Reviewer #2: No

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COVID-19 and Pneumonia Detection and Web Deployment from CT Scan and X-ray Images Using Deep Learning

PONE-D-23-18788R1

Dear Dr. Mohsin,

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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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,

Maleika Heenaye- Mamode Khan

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 #2: All comments have been addressed

Reviewer #3: 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 #2: Yes

Reviewer #3: Yes

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

Reviewer #2: I Don't Know

Reviewer #3: Yes

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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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: Yes

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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 #2: The comments have been addressed, the presentation has been updated and the language has been reviewed.

Minor edit

on Page 8, i. Data Preprocessing to be removed since this section has been moved.

on Page 22, in the last paragraph of the conclusion - using computer vision.to detect COVID-19 - the '. ' to be removed between vision and to

Reviewer #3: Dear Sir,

I hope this message finds you well. I wanted to express my gratitude for your diligent efforts in addressing the feedback provided for the review of your journal paper. I have thoroughly reviewed the revised abstract and conclusion, and I'm pleased to inform you that all the comments have been effectively addressed. The revised conclusion now effectively highlights the significant outcomes of your study, emphasizing the high accuracies achieved by different deep learning models and the validation process using supplementary datasets. Moreover, the addition of insights into the implications of your findings and potential future directions adds depth and relevance to the conclusion.

Furthermore, a small suggestion would be rearding the inclusion of different ablations leading to the conclusion of using optimal hyperparameters. This addition would indeed enhance the comprehensiveness of the study and provide valuable insights into the model selection process.

Looking forward to seeing the finalized version of your paper.

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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 #2: No

Reviewer #3: No

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