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REVIEW 4 major objections 4 minor 46 references

Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A frozen ImageNet-pretrained ResNet-50 used as a fixed feature extractor, followed by Logistic Regression, reaches about 91.5% accuracy on the Monkeypox Skin Images Dataset, and the paper contends that earlier 97–98% state-of-the-art…

desk verdict The leakage critique is the real contribution; the paper's own 91.49% is a CV mean, not a hold-out accuracy, so the comparison to prior work does not hold as written. read the letter →

arxiv 2506.06007 v1 pith:PJBLQB5D submitted 2025-06-06 cs.CV cs.AI

Alejandro Puente-Castro , Enrique Fernandez-Blanco , Daniel Rivero , Andres Molares-Ulloa This is my paper · ORCID
classification cs.CVcs.AI
keywords orthopoxvirusmonkeypoxskinlesionclassificationtransferlearningfeatureextractionlogisticregressiondataaugmentationleakage
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that classifying orthopoxvirus skin images does not require training a large deep network or generating synthetic data. A frozen ResNet-50 pretrained on ImageNet, used only as a feature extractor, followed by a simple Logistic Regression classifier, reaches about 90% accuracy and a Cohen's Kappa around 71% on the original Monkeypox Skin Images Dataset, and about 91.5% accuracy when the training data are augmented. The paper further claims that the 97.61% and 98.64% accuracies reported by two earlier studies are inflated because those studies applied data augmentation before splitting, so their test sets contained near-duplicates of training images. If correct, this makes a cheap, auditable pipeline the appropriate baseline for this dataset and casts doubt on reported state-of-the-art numbers.

What carries the argument

The mechanism is the hybrid pipeline: a frozen ResNet-50 (trained on ImageNet, last layer removed) converts each 224x224 image into a 100,352-dimensional feature vector, and then a lightweight classifier - Logistic Regression, MLP, or SVM - is trained on those vectors. Evaluation uses stratified 10-fold cross-validation over 90% of the dataset (693 images) plus a fixed, held-out stratified test set of 77 images, with Cohen's Kappa as the primary metric because accuracy is misleading on the imbalanced four classes (normal, measles, chickenpox, monkeypox). Two preprocessing variants are added for comparison: SMOTEENN to balance the training set, and random augmentation (vertical flip and Gaussian blur, applied only to training) to enlarge it. The same fixed test set is used for every configuration, which the paper cites as the guarantee of impartial comparison.

What would settle it

Re-evaluate the checkpoints released with Bala et al. [6] and Maqsood et al. [27] on the 77 original, non-augmented images used as this paper's test set: if either model still scores near 97-98%, the leakage claim fails, and if accuracy collapses to the ~90% range, it is supported. A complementary test is to re-run this paper's pipeline with nested cross-validation so the fixed test set is touched only once; if Kappa drops materially below 71%, repeated use of the test set inflated the reported results.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that deep features extracted once by a frozen ImageNet-pretrained ResNet-50 make orthopox image classification tractable for simple classifiers: Logistic Regression attains the best balance of accuracy (90.00%) and Kappa (71.19%) on the original dataset, and neither SMOTEENN balancing nor six-fold data augmentation changes results by more than a few points. The paper also documents that the two best-performing prior studies, Bala et al. and Maqsood et al., drew their test sets from augmented data (1,738 images) instead of the original dataset, so their reported 97.61% and 98.64% accuracies do not reflect independent generalization; on a clean, stratified 77-image test set the proposed pipeline reaches at most 91.49%. The paper concludes that the apparent performance gap is an artifact of test-set contamination, not a real superiority of complex models.

Load-bearing premise

The evaluation assumes that the fixed 77-image test set can be used repeatedly to pick the best model configuration and still count as unseen data; if the test set influenced the choice of classifier or hyperparameters, the reported accuracies overstate generalization.

Editorial extensions

If this is right

  • The two highest published accuracies on the Monkeypox Skin Images Dataset should not be cited as state of the art without first checking whether their test sets were produced by augmentation before splitting; this paper provides the evidence of that leakage.
  • A linear classifier on frozen, pretrained deep features is a strong and cheap baseline for small medical image datasets, likely beating many bespoke deep models at a fraction of the training cost.
  • Data augmentation should be applied only after the training/validation/test split, because any accuracy gain from augmenting the full dataset and then splitting is not a real gain.
  • Balancing and augmentation give diminishing returns: increasing training size by 34% with SMOTEENN or by 600% with augmentation changed Kappa by only a few points, so model capacity is not the bottleneck on this dataset.
  • The proposed method's generalization claims rest on a single 77-image test set, and point estimates like 91.49% carry wide confidence intervals at that size.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same augment-then-split leakage the paper documents for Bala et al. and Maqsood et al. likely affects other small medical imaging benchmarks, so those reported numbers should be audited in the same way before being used as baselines.
  • Because frozen features already saturate performance on this dataset, expensive fine-tuning of large models is probably unnecessary here; a testable extension is to compare fine-tuned ResNet-50 against the frozen-feature pipeline under nested cross-validation.
  • The fixed test set was reused to select the best configuration, so a natural follow-up is to set aside a second, never-touched test set and check whether the ~71% Kappa survives, or to report a confidence interval for the 77-image accuracy.
  • For clinical deployment the four-class accuracy matters less than the confusion pattern between monkeypox and the other rash classes; reporting per-class errors would show whether mistakes are clinically safe, which is where this dataset's next evaluation should focus.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a hybrid pipeline for classifying four classes in the Monkeypox Skin Images Dataset (MSID): a frozen ImageNet-pretrained ResNet-50 extracts deep features, which are then used to train lightweight classifiers (Logistic Regression, SVM, MLP). Three training-condition experiments are compared: original data, SMOTEENN-balanced data, and data augmentation applied only to the training split. The authors report that Logistic Regression on augmented features attains the best mean cross-validation accuracy (91.49%) and use this value to claim near-state-of-the-art performance, while arguing that the higher accuracies reported by Bala et al. (97.61%) and Maqsood et al. (98.64%) are inflated because their test sets were drawn from augmented data. The paper also provides a GitHub link to source code.

Significance. If the evaluation were sound, the paper would make a useful practical contribution: a computationally cheap, interpretable, and auditable pipeline that does not require fine-tuning a deep network, plus a concrete warning about data leakage via test-set augmentation in prior MSID studies. The authors deserve credit for publishing hyperparameter details, making code available, and emphasizing Cohen's Kappa rather than raw accuracy. However, the central quantitative claim is currently not established because the reported headline number is a cross-validation mean, not a hold-out test accuracy, and the fixed 77-image test set appears to have been reused for model selection. These issues materially affect the comparison with prior work and the generalization claims, so the paper needs a substantial revision of its evaluation and reporting before the conclusions can be accepted.

major comments (4)
  1. [Section 3.5 and Section 4.1] The evaluation protocol does not support the claim of an unbiased final hold-out evaluation. Section 3.5 states that all model configurations are continuously evaluated on the fixed 77-image test set, and Section 4.1 states that the best configuration is selected after inspecting results. Any selection based on that fixed set consumes its information, so the test set is not truly unseen for the selected model. Please report the performance of a single final model on a hold-out set that was never used during development (e.g., a nested cross-validation or a strict three-way split), or present hold-out results with the selection procedure explicitly accounted for.
  2. [Table 9 and Sections 4.2.3/5] The central comparison of the proposed method (91.49%) with Bala et al. (97.61%) and Maqsood et al. (98.64%) is not valid as presented. The 91.49% is the mean accuracy over 10-fold cross-validation folds reported in Table 7, not an accuracy measured on the fixed 77-image hold-out set, while the prior values are single test-set accuracies. Cross-validation means over models trained on overlapping 90% subsets are not directly comparable to a single split, so the comparison cannot support the paper's conclusion that prior results are inflated. Please report the accuracy and confidence intervals of the final selected model on a properly held-out set and use that number, not the CV mean, in Table 9.
  3. [Sections 3.5, 4.2, and 5] The claim that results are 'assessed on truly unseen data' (Section 5) is not supported by the evidence, because Tables 3-8 report only cross-validation fold statistics (mean +/- std or SE) and no table reports metrics computed on the 77-image final test set. Please add a table with hold-out accuracy, precision, recall, F1, and Kappa, with confidence intervals, for all configurations, and clearly label in the text which numbers are CV estimates and which are hold-out estimates.
  4. [Section 4.2.1 (and corresponding paragraphs)] The Mann-Whitney U tests are applied to distributions of Cohen's Kappa obtained from the 10 cross-validation folds. These fold-level values are not independent observations: the training sets overlap across folds and the same fixed test set is reused. The independence assumption of the Mann-Whitney U test is therefore violated. Please use a statistical test or resampling procedure that accounts for the dependence structure, or base the tests on independent test-set evaluations.
minor comments (4)
  1. [Table 2] Table 2 lists 'Vertical flipping with 50% probability' in the Data Augmentation row but then states 'Vertical flipping was excluded'; the text in Section 3.2 and Figure 2 also say vertical flipping is excluded. Please resolve this contradiction.
  2. [Throughout] There are several typos and inconsistencies: 'Propossed method' in Table 9, 'SMOTEEN' in the Table 6 heading (should be SMOTEENN), 'EE' in the Table 8 caption (should be SE), and '4.158' in Section 4.2.3 should be '4,158'.
  3. [Abstract and Section 4.2.3] The abstract says the method works 'without the need for augmented data,' but the experiments include a data-augmentation condition and the best reported mean accuracy is from that condition. Please clarify that strong performance can be obtained without augmentation, while acknowledging that augmentation gives a small improvement.
  4. [Section 3.3 and Section 4.1] The paper states that 100,352 features are extracted per image from ResNet-50. Please specify the exact layer and pooling strategy (e.g., the 7x7x2048 spatial features before global pooling) so the feature dimensionality is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method is an empirical benchmark against an external public dataset, and the only self-citation (reference [35]) supports choice of standard metrics and is not load-bearing.

full rationale

This paper makes no formal derivation whose conclusion is equivalent to its assumptions. The central empirical claim is that an ImageNet-pretrained ResNet-50 feature extractor with simple classifiers, particularly Logistic Regression, achieves competitive accuracy on the Monkeypox Skin Images Dataset (MSID), an external public dataset with independent prior results. The 91.49% figure quoted in Table 9 is the 10-fold cross-validation mean from Table 7, not a hold-out test accuracy, and the paper's comparison of this number with prior single-split test accuracies is a methodological comparability concern, not circularity: the number is not obtained by fitting a parameter and then predicting the same fitted quantity. The only self-citation is reference [35], used in Section 3.5 to justify standard evaluation metrics (accuracy, recall, precision, F1, Cohen's Kappa); those formulas are textbook definitions and do not feed back into the results. The claim that Bala et al. and Maqsood et al. overestimate performance due to test-set augmentation is based on the test-set sizes and split tables quoted from those publications (Table 10), not on a self-referential argument. The repeated use of a fixed 77-image hold-out set for model selection, and the absence of reported hold-out accuracy, are validity limitations but do not make any prediction equivalent to its inputs by construction. No load-bearing step reduces to a self-citation, ansatz imported from the authors' own prior work, or renamed known result. Score 0 is therefore appropriate.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No invented entities are introduced. The main debts are the pretrained ResNet-50 feature extractor, the public MSID labels, and the evaluation-protocol assumptions listed above. The free parameters are standard classifier hyperparameters and augmentation choices, but their search ranges and random seeds are not reported, which makes the tuning process hard to audit.

free parameters (5)
  • Logistic Regression inverse regularization strength (C) = 0.1
    Chosen through a randomized parameter search (Table 2) and used for the recommended model in all three data conditions.
  • MLP hidden layer size and L2 regularization = 100 neurons, alpha=0.05
    Chosen through the same randomized search; MLP has the highest mean Kappa in the augmented condition.
  • SVM regularization strength and kernel = C=100, sigmoid kernel
    Chosen through the randomized search; SVM consistently underperforms the other two classifiers.
  • SMOTEENN nearest-neighbor count = k=5
    Controls how many synthetic minority samples are generated in the balanced condition; no sensitivity analysis is given.
  • Data augmentation schedule = 6 new images per training image; vertical flip 50%; Gaussian blur kernel 5 with 50% probability
    Determines the 4,158 extra training images in the augmented condition; random seed is not specified.
assumptions (5)
  • domain assumption The Monkeypox Skin Images Dataset (MSID) is a valid, clinically meaningful benchmark.
    All results inherit the data's quality, label correctness, and selection biases; the paper does not independently validate labels.
  • domain assumption ImageNet-pretrained ResNet-50 features transfer to dermatology images without fine-tuning.
    The pipeline treats ResNet-50 as a frozen extractor (Section 3.3); if the domain gap is large, the extracted features would be weaker.
  • domain assumption The 77-image stratified hold-out set is representative of the clinical population.
    One tenth of 770 imbalanced images is small; per-class performance estimates will have wide variability.
  • domain assumption Mann-Whitney U tests are valid when applied to scores from 10-fold cross-validation folds.
    Fold scores are not independent because training sets overlap; the test assumption of independence is likely violated (Section 4.1).
  • ad hoc to paper The fixed test set can be reused for configuration selection and still be considered unseen.
    Section 3.5 evaluates all configurations on the fixed test set and Section 4.1 selects parameters afterward; no justification is given for why this does not bias the results.

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Cite this review

Pith. "Pith review of Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models." pith.science (2026). https://pith.science/paper/PJBLQB5D

@misc{pith2026250606007,
  author       = {Pith},
  title        = {Pith review of: Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PJBLQB5D}},
  note         = {Machine review of arXiv:2506.06007}
}
read the original abstract

Orthopoxvirus infections must be accurately classified from medical pictures for an easy and early diagnosis and epidemic prevention. The necessity for automated and scalable solutions is highlighted by the fact that traditional diagnostic techniques can be time-consuming and require expert interpretation and there are few and biased data sets of the different types of Orthopox. In order to improve classification performance and lower computational costs, a hybrid strategy is put forth in this paper that uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data. The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes while preserving training and inference efficiency. The proposed approach demonstrates strong generalization and robustness across multiple evaluation settings, offering a scalable and interpretable solution for real-world clinical deployment.

Figures

Figures reproduced from arXiv: 2506.06007 by the authors.

Figure 1
Figure 1. Examples for each class from the original dataset [ [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Example of an image from the dataset [6] vertical flipping is avoided to preserve anatomical realism, as such transformation would not reflect real-world conditions or would not create new observations of the disease [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Scheme with the design of the hybrid model for image processing. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Diagram with the partition of the dataset for training and evaluating each model configuration in each [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Boxplot of the distributions of the Cohen’s Kappa values for each model trained with the original dataset. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Boxplot of the distributions of the Cohen’s Kappa values for each model trained with the dataset balanced [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Boxplot of the distributions of Cohen’s Kappa values for each model trained with the dataset augmented [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Boxplot of the distributions of Cohen’s Kappa of the three experiments. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.