PONE-D-24-03396Automated Brain Tumor Diagnostics: Empowering Neuro-Oncology with Deep Learning-Based MRI Image AnalysisPLOS ONE

Dear Dr. Shah,

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We look forward to receiving your revised manuscript.

Kind regards,

Toqeer Mahmood, Ph.D.

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?

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

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: 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: 1. Pseudocode / Flowchart and algorithm steps need to be inserted.

2. Time spent need to be measured in the experimental results.

3. Limitation and Discussion Sections need to be inserted.

4. All metrics need to be calculated such as Recall, LossROC curves and AUC score as a table in the experimental results.

5. The parameters used for the analysis must be provided in table

6. The architecture of the proposed model must be provided

7. Address the accuracy/improvement percentages in the abstract and in the conclusion sections, as well as the significance of these results.

8. The authors need to make a clear proofread to avoid grammatical mistakes and typo errors.

9. Enhance the clarity of the Figures by improving their resolution.

10. Add future work in last section (conclusion) (if any)

11. The authors need to add recent articles in related work and update them.

12. To improve the Related Work and Introduction sections authors are recommended to review this highly related research work paper:

a) Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach

b) Optimizing epileptic seizure recognition performance with feature scaling and dropout layers

c) Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

d) Optimizing classification of diseases through language model analysis of symptoms

e) Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction

f) Utilizing convolutional neural networks to classify monkeypox skin lesions

g) Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt

Reviewer #2: 1- Explaining the researcher’s contributions in clear and sequential points

2- The references listed in Related Works are not arranged according to year of publication (from oldest to newest). In addition to clarifying the strengths and weaknesses of each research paper

3- Explain the proposed work algorithm in points before beginning to explain each point in detail, as was done in the research paper

4-Conclusions need to be supported by numerical values from the results

5-Standardize the format of references

Reviewer #3: Some comments for the authors to improve the quality of the article

1- Explain the abbreviation in the first use.

2- What does ResNeXt101 stand for? Usually, the ResNeXt10 algorithm is used for 1D data and 2D. How is the visualization of the image data after applying the ResNeXt10 algorithm.

3- The “Introduction” and “Related Work” sections lack of enough references. I strongly recommend that the author improve this section by adding references that support all the claims and motivation of the problem. The author may precisely and comprehensively point out the current issues and existing solutions. I suggest adding more related reference such as:

a- https://ieeexplore.ieee.org/abstract/document/10463420

b- https://ieeexplore.ieee.org/abstract/document/8876847

c- https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27938

d- https://jou.jobrs.edu.iq/index.php/home/article/view/63

e- https://www.informatica.si/index.php/informatica/article/view/4840

f- https://link.springer.com/article/10.1186/s13673-019-0191-8

g- https://ieeexplore.ieee.org/abstract/document/10086271

4- Why are the results in Table 3 different from those in Table 4?

5- Does the ResNeXt10 algorithm perform before or after splitting the data? In the research methodology, the process of data splitting was not explained

6- Authors should add the computational complexities and cost for all reviewed works.

6- How authors tuned the hyperparameters?

7- Limitations and the future scope should be added with more clarity.

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Reviewer #1: Yes: Tarek Abd El-Hafeez

Reviewer #2: No

Reviewer #3: No

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Automated Brain Tumor Diagnostics: Empowering Neuro-Oncology with Deep Learning-Based MRI Image Analysis

PONE-D-24-03396R1

Dear Dr. Shah,

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,

Toqeer Mahmood, Ph.D.

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: Yes

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: (No Response)

Reviewer #3: The authors have done all the necessary revisions well. I recommend accepting the article for publication

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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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