PONE-D-21-24533

Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray Images

PLOS ONE

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Xiaodi Huang, PhD

Academic Editor

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?

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This paper aims to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Authors evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. They have developed and trained CNN from scratch. A recently published public X-Ray dataset for training and validation purposes is used. The comprehensive ROC and confusion metric analysis with 10-fold cross-validation strongly demonstrate the ideas and methods developed in the paper. Experiment results are compared to demonstrate the effectiveness of models in classification scenarios and their potential for COVID-19 classification, detection, prevention, and control.

The paper is well organised with a clear motivation, innovation and also rich experiments. One minor suggestion is to have two paragraphs in the final section for conclusion and future work.

Reviewer #2: In this manuscript, authors have proposed and presented a CNN oriented COVID-19 detection method, which combine transfer learning and CNN-based model and achieves satisfied results. The idea is interesting. The intention of this study is to evaluate Deep Learning methods for the diagnosis of COVID-19 from X-Ray images. Specifically, both state-of-the-art pretrained CNNs (e.g., VGG) and hand-crafted CNN are utilized either for transfer learning or training from scratch. A relatively small image dataset (at least for the COVID-19 class) is utilized and the results confirm the findings of several related research.

Nevertheless, there are some issues I would like to point out to enrich the contribution, and the proposal description, which I think must be attended to:

• Authors must highlight the contribution in a more profound way to identify the analysis of the several sources consulted.

• Please explain what is CNN II “Therefore, we build a new CNN II which yielded accuracy 97.62% in binary classification” in section 4.2. Probably authors meant Scenario II. Clarify it.

• In Table 7, the necessary space is missing, which is a little confusing. If possible, rearrange the Table 7.

• More details need to be presented to show the significance of the results presented in Table 8 and 9.

• Finally, there are some grammatical and structural errors that need to be rectified in the revised version of this manuscript.

Overall, good work has been presented.

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Reviewer #1: Yes: Jianming Yong

Reviewer #2: No

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Automated Detection of COVID-19 through Convolutional Neural Network using Chest x-ray Images

PONE-D-21-24533R1

Dear Dr. Wang,

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.

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Kind regards,

Xiaodi Huang, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I have read the modified version of this submission. All the minor issues raised by the reviewers have been addressed.

Therefore, I recommend to publish this paper.

Reviewers' comments: