-->PONE-D-25-66974-->-->AI-Driven Diagnosis of Monkeypox using Deep Learning Models-->-->PLOS One

Dear Dr. Sadek,

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Additional Editor Comments:

PLOS ONE Editorial Decision

Manuscript Number: PONE-D-25-66974

Title: AI-Driven Diagnosis of Monkeypox using Deep Learning Models

Authors: Bassam W. Aboshosha, Shafiq Ul Rehman, Lamees N. Mahmoud, Ibrahim Sadek

Dear Dr. Sadek and colleagues,

Thank you for submitting your manuscript to PLOS ONE. I have now completed a thorough assessment of your submission, including the two reviewer reports and my own detailed evaluation. The manuscript addresses a timely and clinically relevant topic, the application of deep learning for mpox diagnosis using skin image analysis. The proposed integration of this diagnostic capability into an “unmanned smart clinic” concept demonstrates awareness of real-world deployment challenges, particularly in resource-limited settings. These elements represent genuine strengths. However, after careful review, I must concur with the reviewers that the manuscript in its current form does not meet the standards for publication in PLOS ONE. The concerns raised are fundamental and extend beyond minor revision.

Summary of Critical Issues

1. The Methodological Contribution is Insufficient

Reviewer 1's assessment is direct and, in my judgment, accurate: “There is no novelty in the methodology. This study is totally relied on established architectures without any significant advancement.” The manuscript evaluates five standard pre-trained CNN architectures (MobileNet, DenseNet121, ResNet50, Inception-v3, EfficientNet) using a publicly available Kaggle dataset. The core methodology, transfer learning with fine-tuning on medical images, is well-established and has been extensively applied to mpox detection in the literature, as the authors' own extensive reference list demonstrates (over 45 cited studies on this exact topic).

The critical question: What does this study add that is not already present in the existing literature? The authors do not articulate a clear methodological innovation, a novel architectural modification, a new training strategy, or a substantive advance in how these models are applied. Without such a contribution, the manuscript reads as a replication study, which, while potentially valuable, does not constitute the original research expected by PLOS ONE.

2. The Literature Review is Incomplete and the Contribution is Unclear

Reviewer 2 correctly notes that the literature review "doesn't provide comprehensive coverage of related work" and identifies a “significant gap in coverage of the state-of-the-art.”

This criticism is particularly salient given the sheer volume of existing work. The authors cite over 45 studies on AI-based mpox detection, yet the manuscript does not:

• Synthesize what these studies have collectively established

• Identify persistent gaps or limitations in the existing literature

• Clearly articulate how the present study addresses those gaps

• Demonstrate why another comparative evaluation of standard models is needed

The introduction states the study's contributions as: (1) developing AI-driven diagnostic models, (2) proposing a smart unmanned medical clinic concept, (3) presenting a comparative analysis, and (4) supporting AI-enhanced healthcare. However:

• Contribution (1) is not novel, dozens of studies have already developed such models

• Contribution (2) is a conceptual proposal, not empirically validated in this study

• Contribution (3) is descriptive, not analytical, it does not generate new insights about why certain models perform better or how they could be improved

• Contribution (4) is a general aspiration, not a specific finding

The manuscript needs a clearly articulated research gap and a specific research question that this study answers. Without this, the contribution remains unclear.

3. The Dataset Description is Inadequate

Reviewer 2 notes: “There is no such sub-section mentioning the details about the dataset size. I couldn't find anything regarding the size or quantity of the dataset.”

This is a serious omission. Table 2 provides class distributions for training, validation, and test sets, but critical information is missing:

• Source: The authors state the dataset was obtained from Kaggle and “has previously been used in research.” Which specific Kaggle dataset? What is its original source? How were images collected? What are the inclusion/exclusion criteria?

• Verification: “Each image of a skin lesion was double-checked using references and Google's reverse image search.” This is insufficient for clinical-grade data. What were the verification criteria? Who performed the verification? What was the inter-rater reliability?

• Characteristics: What are the image resolutions? What are the demographic characteristics of the patients represented? What are the lesion locations, stages, and presentations? Are there multiple images per patient?

• Confounders: Are there potential confounders such as image quality differences between classes, background variations, or lighting conditions that could introduce bias?

Without this information, the validity of the results cannot be assessed. The near-perfect performance of EfficientNet (99.64% accuracy) raises concerns about potential data leakage, overfitting, or dataset biases that cannot be evaluated without detailed dataset documentation.

4. The Experimental Details are Insufficient for Reproducibility

Reviewer 2 raises multiple concerns about experimental transparency:

• “It is not clear how the authors used the embedding techniques.”

• “It is not clear how the authors fine-tuned the model.”

• “It is not clear how the parameters of the models are compared.”

Critical missing information includes:

• Preprocessing details: Beyond “poor-quality images were eliminated,” what were the specific criteria? What preprocessing steps were applied (resizing, normalization, color space conversion)?

• Augmentation details: Which specific augmentations were applied? With what parameters? Was augmentation applied only to training data, or also to validation/test data?

• Training details: What was the optimization algorithm? Learning rate? Batch size? Number of epochs? Early stopping criteria? Loss function?

• Fine-tuning details: Which layers were frozen vs. fine-tuned? What were the initial and final layer configurations?

• Hardware/software: What computing infrastructure was used? What frameworks and versions?

• Hyperparameter selection: How were hyperparameters chosen? Was there a validation-based selection process?

Without these details, the experiments cannot be reproduced or critically evaluated.

5. The Reported Performance Raises Concerns

EfficientNet achieving 99.64% validation accuracy with 100% precision and recall (zero false positives, zero false negatives) on a medical image classification task is extraordinarily high.

This level of performance warrants critical scrutiny:

• Class imbalance: The dataset is nearly balanced (1,428 mpox, 1,376 normal), so imbalance is not an explanation.

• Dataset size: 2,804 images is modest for deep learning. With perfect performance, one must consider whether the test set is too easy, whether there is data leakage between train/validation/test, or whether the dataset contains systematic biases that the model has learned.

• Comparison to literature: The authors' own Table 4 shows that most published studies report accuracies in the 82-98% range, with only a few achieving >99%. What explains this study's superior performance?

The manuscript does not address these concerns. There is no analysis of difficult cases, no error analysis (because there are no errors), no discussion of potential dataset limitations, and no external validation.

6. The “Unmanned Smart Clinic” Concept is Underdeveloped

The proposal of an unmanned smart clinic is conceptually interesting but:

• It is not integrated with the core analysis. The clinic concept is described in Section 3, but the AI models are evaluated in isolation. There is no demonstration or validation of how these models would function within such a clinic.

• Operational details are missing. How would image capture be standardized? What would prevent users from submitting poor-quality images? How would the system handle uncertainty or edge cases? What would be the workflow for positive vs. negative results?

• Implementation challenges are not addressed. Connectivity, data privacy, regulatory approval, user acceptance, and integration with existing health systems are not discussed.

As presented, this section reads as a conceptual illustration rather than a substantive contribution.

7. The Literature Comparison Table is Problematic

Table 4 attempts to compare this study with existing work, but it contains multiple issues:

• Inconsistent reporting: Some entries report accuracy with percentages, others without. Some include confidence intervals, most do not.

• Missing context: The table does not indicate dataset sizes, class distributions, or validation methodologies, making comparisons meaningless.

• Self-citation: The table includes “This study” at the end, but the comparison is superficial.

• Errors: Reference numbering in the table appears inconsistent with the reference list.

A proper comparison would require standardized metrics, dataset characteristics, and validation approaches, none of which are provided.

8. The Writing and Presentation Require Substantial Improvement

Beyond the methodological issues, the manuscript has significant presentation problems:

• Redundancy: The introduction repeats information about mpox symptoms and transmission across multiple paragraphs.

• Organization: Section 4 (“Methodology”) includes subsections that could be better structured. The mathematical notation in lines 46-136 appears to be from a different manuscript (it references “this section” and includes theorems that are not used in the analysis).

• Figures: Figures 8-14 are difficult to interpret. Some are low-resolution, others have illegible text. The precision-recall curves lack threshold annotations.

• Table formatting: Table 1 extends across multiple pages with inconsistent formatting. Table 3 presents metrics without confidence intervals.

• References: The reference list is overly long (48 references) but not well-integrated into the text. Many references are cited in blocks without specific attribution.

Specific Required Revisions

If the authors wish to resubmit, the following must be addressed comprehensively. This represents a major revision at minimum, and the authors should carefully consider whether the current study can be revised to meet the required standards.

A. Clarify the Novelty and Contribution (Required)

• Conduct a thorough literature review and clearly articulate the specific gap this study addresses.

• State explicit research questions or hypotheses.

• Demonstrate what this study adds beyond existing work, methodological innovation, new insights, or practical advances.

B. Provide Complete Dataset Documentation (Required)

• Specify the exact Kaggle dataset (URL, version, source).

• Provide detailed inclusion/exclusion criteria for image selection.

• Report image characteristics (resolution, format, quality metrics).

• Describe the verification process in detail, including who performed it and how reliability was ensured.

• Disclose any potential confounders or biases in the dataset.

C. Provide Complete Experimental Details (Required)

• Document all preprocessing steps with specific parameters.

• List all augmentation techniques with parameters and rationale.

• Report training hyperparameters (optimizer, learning rate, batch size, epochs, loss function).

• Describe fine-tuning strategy (which layers frozen, learning rates for different layers).

• Specify hardware and software environment.

• Detail the hyperparameter selection process.

D. Address Performance Concerns (Required)

• Conduct and report cross-validation results (not just a single train/validation/test split).

• Provide confidence intervals for all performance metrics.

• Report performance on multiple metrics, including sensitivity, specificity, PPV, NPV, and F1-score for each class.

• Analyze difficult cases, what images were misclassified (if any) and why?

• If performance remains perfect, discuss potential explanations and limitations.

E. Validate on External Data (Strongly Recommended)

• Test the models on an independent dataset not used in development.

• If external validation is not possible, explicitly discuss this as a major limitation.

F. Develop the Clinic Concept or Remove It (Required)

• Either integrate the clinic concept with empirical validation (e.g., testing the models under simulated clinic conditions, analyzing image quality variations), or remove it and focus on the core diagnostic analysis.

• If retained, address operational, regulatory, and implementation challenges.

G. Revise the Literature Comparison (Required)

• Create a meaningful comparison table with standardized metrics and dataset characteristics.

• Discuss why performance differs across studies.

• Acknowledge limitations in cross-study comparisons.

H. Improve Writing and Presentation (Required)

• Restructure the manuscript with clear sections: Introduction, Related Work, Methods, Results, Discussion, Conclusion.

• Remove redundant content.

• Ensure all figures are high-resolution with legible text and self-explanatory captions.

• Reformatted tables for clarity and consistency.

• Review and revise references to ensure accurate citation and formatting per PLOS ONE style.

I. Address Reviewer 2's Specific Concerns (Required)

• Add objectives and motivations tied to literature gaps.

• Add research questions.

• Cite and discuss missing relevant literature (e.g., the works suggested by Reviewer 2).

• Discuss challenges in the work and the dataset.

• Identify avenues for future research.

Decision: Major Revision Required

Given the above, I cannot recommend acceptance in the current form. The manuscript does not meet PLOS ONE's standards for methodological rigor, novelty, transparency, or reproducibility.

The authors may choose to resubmit a substantially revised manuscript addressing all of the concerns outlined above. Any resubmission will be evaluated de novo and will be sent for additional peer review. Given the fundamental nature of the concerns, particularly regarding novelty, the authors should carefully consider whether the revised manuscript can demonstrate a clear contribution. If the authors believe these concerns cannot be adequately addressed, they may wish to consider alternative venues more appropriate for replication studies or methodological validations. I thank the authors for their work on an important public health problem and encourage them to consider how their approach might be advanced to make a more distinctive contribution.

Sincerely,

Morufu Olalekan Raimi, 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?

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

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

Reviewer #1: No

Reviewer #2: N/A

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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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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: There is no novelty in the methodology. This study is totally relied on established architectures without any significant advancement. The paper is not suitable for a reputable journal like plos one.

Reviewer #2: The manuscript presents several significant issues that need to be addressed before it can be considered for publication. Please find my detailed comments and concerns below:

1. Please add two paragraphs in the introduction: a) objectives and motivations tied to gaps in the literature; b) research questions.

2. The literature review of the article doesn't provide comprehensive coverage of related work. Most troublesome is the significant gap in coverage of the state-of-the-art. A few important pieces of literature about deep learning are not cited, for example.

(a). Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks, DOI: https://link.springer.com/article/10.1007/s11042-024-18437-z

3. It is not clear how the authors used the embedding techniques. Please provide more explanations.

4. It is not clear how the authors fine-tuned the model. Please add experiments and details.

5. It is not clear how the parameters of the models are compared.

6. I would strongly advise including all major works in 2023 and 2024-25 and drawing a tabular comparison between your work and other works. A few works worth comparing, referring to, or citing are below:

(b). A CNN-LSTM-Based Hybrid Deep Learning Approach for Sentiment Analysis on Monkeypox Tweets

DOI: https://link.springer.com/article/10.1007/s00354-023-00227-0

7. There is no such sub-section mentioning the details about the dataset size. I couldn’t find anything regarding the size or quantity of the dataset.

8. I'd recommend adding some possible improvements for the proposed approach.

9. What are the avenues for future research or improvements identified based on the findings of this study?

10. The challenges in the work need to be stated (As mentioned in the title).

11. What are the challenges in the dataset?

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-->6. 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: Yes:  Dr. Tayyaba Anees

Reviewer #2: Yes:  Dr. Gaurav Meena

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-->PONE-D-25-66974R1-->-->AI-Driven Diagnosis of Monkeypox using Deep Learning Models-->-->PLOS One

Dear Dr. Sadek,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 15 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:-->

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• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
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-->

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We look forward to receiving your revised manuscript.

Kind regards,

Morufu Olalekan Raimi, Ph.D

Academic Editor

PLOS One

Journal Requirements:

1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

PLOS ONE Editorial Decision

Manuscript ID: PONE-D-25-66974_R1

Title: AI-Driven Diagnosis of Monkeypox using Deep Learning Models

Authors: Aboshosha et al.

Editor: Dr. Morufu Olalekan Raimi

Decision: Minor Revision

Summary of Evaluation

The authors have submitted a substantially revised manuscript that directly and comprehensively addresses the concerns raised in the previous round of review. The original submission suffered from a lack of methodological novelty, inadequate dataset documentation, insufficient experimental detail, near perfect but implausible performance metrics, and an underdeveloped “smart clinic” concept. The revised version has been fundamentally restructured. The paper is now explicitly framed as a methodological benchmark rather than an architectural contribution. It provides a leakage aware, group stratified five fold cross validation protocol on a unified dataset from MSLD v1.0 and v2.0. The smart clinic concept has been removed, near perfect results replaced by conservative but credible estimates (ensemble F1 = 0.8334, AUC = 0.9388), and all previously missing experimental details (preprocessing, augmentation, hyperparameter search, fine tuning, hardware/software, seeds) are now fully documented. The authors also provide a perceptual hash near duplicate audit, calibration analysis, threshold verification, test time augmentation ablation, and multiple statistical tests with Bonferroni correction. Given the rigor of the revisions, the manuscript now meets PLOS ONE’s standards for methodological transparency and reproducibility. However, a few remaining issues require attention before final acceptance.

Required Minor Revisions

1. Title inconsistency

The main manuscript (pages 17 and 63) uses “Mpox” and “Monkeypox” interchangeably in the title and abstract. Please standardize to “Mpox” throughout, consistent with current WHO terminology.

2. Table numbering discrepancies

In the point by point response, the authors refer to “Table 3” for augmentation parameters and “Table 4” for complexity, but in the manuscript, augmentation hyperparameters appear in Table 5 and complexity in Table 7. Please reconcile all cross references in the response letter to match the final manuscript’s table numbering.

3. Figure 1 caption clarity

Figure 1 (dot and whisker plot) is well constructed, but the caption should explicitly state that points are mean values across five folds and whiskers represent ±1 standard deviation. This is implied but not fully spelled out.

4. External validation statement

The Discussion correctly lists the lack of external validation as a limitation. However, the Conclusion currently states that “future work should extend this line of research through externally validated datasets.” Please add a one sentence acknowledgment in the Abstract as well, e.g., “External clinical validation remains necessary before deployment.”

5. Minor typographical corrections

o Page 41, line 359: “originally test evaluation” → “original only test evaluation”

o Page 73, Table 3: “16 fold test time augmentation” row is correct, but the caption of Table 12 (page 82) mislabels TTA as “×16” – this is fine, but ensure consistent wording (“16 fold” vs. “×16”) across the manuscript.

Editorial Comments

The authors have done an exemplary job responding to the previous criticisms. The revised benchmark is transparent, reproducible, and appropriately conservative. The removal of the smart clinic concept was necessary and correct. The new results are credible and well supported by robustness analyses. The paper now makes a clear methodological contribution to a literature that has suffered from inconsistent evaluation practices. Once the above minor corrections are made, the manuscript will be acceptable for publication in PLOS ONE.

Final decision: Minor revision. No further peer review is anticipated, but the revised manuscript will be checked for compliance with the above items.

Dr. Morufu Olalekan Raimi

PLOS ONE Academic Editor

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AI-Driven Diagnosis of Mpox using Deep Learning Models

PONE-D-25-66974R2

Dear Author,

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,

Morufu Olalekan Raimi, Ph.D

Academic Editor

PLOS One

Additional Editor Comments (optional):

PLOS ONE Editorial Decision

Manuscript ID: PONE-D-25-66974_R2

Title: AI-Driven Diagnosis of Mpox using Deep Learning Models

Authors: Aboshosha et al.

Editor: Dr. Morufu Olalekan Raimi

Decision: Accept

Editorial Assessment

The authors have submitted a revised manuscript (R2) that fully addresses all five required minor revisions from the previous decision letter (R1). I have verified each point:

1. Title terminology – Standardized to “Mpox” throughout, with appropriate retention of “monkeypox virus (MPXV)” and dataset folder names. Compliant.

2. Table numbering – The response letter now correctly references Table 5 (augmentation) and Table 7 (complexity). No remaining discrepancies.

3. Figure 1 caption – Explicitly states that points are mean values across five folds and whiskers represent ±1 standard deviation. Sufficiently clear.

4. External validation statement – Added to the Abstract as requested, closing with: “Accordingly, these results should be interpreted as a reproducible reference benchmark rather than a clinically validated diagnostic tool, and external clinical validation remains necessary before deployment.” Acceptable.

5. Typographical corrections – “original-only test evaluation” now appears consistently; test-time augmentation terminology standardized to “16-fold” across all tables, captions, and running text.

No new issues have been introduced. The manuscript remains methodologically rigorous, transparent, and appropriately conservative in its claims. The benchmark serves as a valuable reference for the mpox imaging community.

Final Decision: Accept. No further revisions required.

Dr. Morufu Olalekan Raimi

Academic Editor, PLOS ONE

Reviewers' comments: