Dear Dr. Coulibaly,

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Kind regards,

Mohammad Alfrad Nobel Bhuiyan, 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?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: N/A

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1:  Review of the Paper: "Improved Swin Transformer based Thorax Disease Classification with Optimal Feature Selection using Chest X-ray"

The paper proposes a novel method for classifying thoracic diseases using a deep learning framework that combines an Improved Swin Transformer for classification, Chaotic Whale Optimization for feature selection, and Enhanced Auto-Encoder for feature extraction. However, there are some restrictions need to be solved.

1. There is uncertainty over the model's applicability in real-world clinical operations. There hasn't been any clinical validation with radiologists.

2. The proposed method only compares with limited number of state-of-the-art methods and also ignores newer methods.

3. There are different optimization algorithms that can be use instead of Chaotic Whale Optimization Algorithm. In the paper, do not mention any reason for using this algorithm. Additionally, ChWO algorithm adds extra computational overhead.

4. There are different number of chest x-ray datasets that can be used for evaluating the approach. Using just one dataset is not enough.

5. The dataset size and detailed information about it are not fully discussed.

6. The best results in the tables do not bold.

7. Overall, the article is not well-published and well-structured, especially in the Result and Discussion section.

Reviewer #2:  Strengths of the Paper:

1. The proposed framework effectively addresses class imbalance through data augmentation, enhancing model generalization.

2. The combination of stacked auto-encoder architecture with an attention module in EnAE improves feature representation and classification accuracy.

3. The utilization of the Chaotic Whale Optimization Algorithm refines feature selection, ensuring optimal attributes are used for classification.

4. The IMSTrans model efficiently processes high-dimensional medical image data, leading to improved classification performance.

5. The experimental results indicate the proposed method achieves high performance metrics, demonstrating its reliability and efficiency in thorax disease classification.

Main Comments for Improvement:

1. Recent References: The paper should include references from 2024, ensuring the literature review is up-to-date and reflects the latest advancements in thoracic disease classification and deep learning applications.

2. Comparison with State-of-the-Art: The results should be compared with recent state-of-the-art methods. Additionally, citations should be included in the comparative analysis to support claims of superior performance.

3. Acronyms Usage: The full form of each acronym should be provided only at its first mention in the text. Subsequent mentions should use only the acronym to maintain readability and consistency.

4. Problem Statement References: The problem statement section should be supported with appropriate references to establish the significance and relevance of the study.

5. Proposed Methodology References: The methodology should be reinforced with citations, particularly when discussing techniques such as EnAE, the Chaotic Whale Optimization Algorithm, SWIN, and IMSTrans, to establish credibility and provide background for the employed techniques.

6. Data Distribution Analysis: The paper does not mention the data distributions before and after augmentation. It is essential to provide details on the original dataset distribution and how augmentation affects class balance and feature diversity.

7. Method Limitations and Future Work: The paper should discuss the limitations of the proposed method, such as computational complexity, dependency on dataset quality, or generalizability to other medical imaging modalities. Additionally, future research directions should be outlined.

Reviewer #3:  Thank you for the opportunity to review " Improved Swin Transformer based Thorax Disease Classification with Optimal Feature Selection using Chest X-ray”.

While the topic of the submitted article of great importance, minor concerns exist regarding the current version of the manuscript.

The introduction needs a clearer articulation of the research gap and objectives.

The literature review should have more critical analysis of prior work. Rather than merely summarizing existing research, the authors should highlight how their study builds upon and differentiates from previous efforts.

The manuscript mentions a publicly available dataset but lacks specifics (e.g., class distribution, preprocessing steps like handling missing or noisy data). Clarify these details to ensure reproducibility.

Provide more details on hyperparameter tuning (e.g., learning rates, batch sizes) and any cross-validation strategies used.

In discussion section, consider including a comparison of findings with prior studies to highlight the novelty and significance of the work.

Some citations (e.g., [21]–[32]) are formatted inconsistently. Ensure all references follow PLOS ONE guidelines.

• "ChextX-Ray dataset" → "ChestX-Ray dataset" (Line # 434).

• "publically" → "publicly" (Line # 191, 434).

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Improved Swin Transformer based Thorax Disease Classification with Optimal Feature Selection using Chest X-ray

PONE-D-24-53299R1

Dear Dr. Coulibaly,

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,

Mohammad Alfrad Nobel Bhuiyan, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: