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REVIEW 5 major objections 6 minor 51 references

Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A late discrete wavelet transform plus three swarm optimizers lifts four pretrained CNN classifiers past 98% accuracy on ISIC-2016 and ISIC-2017 skin-lesion benchmarks.

desk verdict The accuracy gains are plausible but the statistical support is not: the ISIC-2017 p-value tables are identical to the ISIC-2016 ones, and the headline accuracy is misquoted. read the letter →

arxiv 2412.00472 v1 pith:YQHZKAJL submitted 2024-11-30 cs.CV cs.LGcs.NEeess.IV

Ramin Mousa , Saeed Chamani , Mohammad Morsali , Mohammad Kazzazi , Parsa Hatami , Soroush Sarabi This is my paper · ORCID
classification cs.CVcs.LGcs.NEeess.IV
keywords skincancerdiagnosisdiscretewavelettransformswarmoptimizationself-attentiontransferlearningISIC20162017melanomaclassification
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 argues that a comparatively cheap post-processing pipeline can push already-strong deep classifiers over a clinically meaningful accuracy threshold in skin-cancer screening. The authors insert a discrete wavelet transform after the feature maps of four pretrained CNNs, add a self-attention layer, and then replace the ordinary training rule of a small classification head with swarm-based optimizers (Improved Grey Wolf Optimizer, Fox optimizer, Modified Gorilla Troops Optimizer). On ISIC-2016 they report 98.11% accuracy with MobileNet+Wavelet+Fox and DenseNet+Wavelet+Fox, and on ISIC-2017 97.95% with Inception+Wavelet+MGTO, each at least 1% above the comparison methods. If reproducible, the result matters because early and accurate automated screening could catch melanoma before it advances.

What carries the argument

The load-bearing mechanism is the late discrete wavelet transform (DWT) module: it takes the feature maps produced by a pretrained CNN and decomposes each into four sub-bands (LL, LH, HL, HH), preserving low-frequency global structure and high-frequency edges and textures, then concatenates them for a self-attention layer and a dense classification head. Around this, three swarm optimizers—Fox, Improved Grey Wolf Optimizer (IGWO), and Modified Gorilla Troops Optimizer (MGTO)—are used to choose ANN weights and hyperparameters, replacing standard backpropagation-based fine-tuning of the head.

What would settle it

Re-run the paired t-tests on the per-fold accuracies given for ISIC-2017 (Table 7) and compare with Tables 8-11: those tables exactly repeat the ISIC-2016 numbers, so unless the 2017 statistics are recomputed from 2017 folds, the claimed statistical significance is not supported. A simpler check is whether any independent run reproduces the reported 97.95% Inception+Wavelet+MGTO accuracy on the official ISIC-2017 test split.

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Extended reading notes

Core claim

The central claim is that a 'late' discrete wavelet transform applied to CNN feature maps, followed by self-attention and swarm-optimizer-driven weight tuning, improves binary melanoma classification on standard dermoscopy benchmarks beyond prior art. Specifically, the paper reports that MobileNet + Wavelet + FOX and DenseNet + Wavelet + FOX reach 98.11% accuracy on ISIC-2016, Inception + Wavelet + MGTO reaches 97.95% on ISIC-2017, and these results exceed comparison methods by at least 1%. The paper also claims the wavelet stage and optimizer stage each contribute, with the relative benefit varying by backbone.

Load-bearing premise

Every headline comparison assumes that all models were trained and tested on the same fixed data split and that the ISIC-2017 statistical tables were computed on 2017 data; the duplicated p-value tables indicate that this premise is likely violated.

Editorial extensions

If this is right

  • The pipeline can be added to any pretrained CNN by inserting DWT, self-attention, and a swarm-optimized head, so multiple backbones benefit without retraining the trunk.
  • The reported accuracy gains on both ISIC-2016 and ISIC-2017 suggest the approach transfers across datasets of different sizes and difficulty.
  • Since the best optimizer differs by backbone (Fox with MobileNet and DenseNet on ISIC-2016, MGTO with Inception on ISIC-2017), the paper implies optimizer choice should be treated as a per-architecture hyperparameter.
  • The combination offers a route to automated screening tools that could rank suspicious lesions for dermatologist review.

Reading between the lines

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

  • The wavelet stage likely acts as a frequency-domain regularizer that sharpens edge and texture cues and reduces sensitivity to class imbalance, though the paper's ablations do not isolate this mechanism directly.
  • A direct comparison against plain backpropagation fine-tuning of the same head would clarify how much of the gain is due to swarm optimization rather than to additional training on the wavelet and attention features.
  • The ISIC-2017 significance tables numerically duplicate the ISIC-2016 tables, so the statistical-significance claims for the 2017 results need to be re-derived before the improvement can be taken as established.
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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

5 major / 6 minor

Summary. The paper proposes a skin-cancer classification pipeline that combines a pretrained CNN (DenseNet-121, Inception, Xception, or MobileNet), a late discrete wavelet transform (DWT), a self-attention module, and one of three swarm-based optimizers (FOX, IGWO, or MGTO) that tunes a final ANN head. The authors report accuracy up to 98.11% on ISIC-2016 and 97.95% on ISIC-2017 and claim improvements of at least 1% over prior methods. The evaluation is based on 5-fold cross-validation tables and pairwise t-test significance tables, with public code and data availability stated.

Significance. If the reported numbers were reproducible under a clean evaluation protocol, the paper would offer a useful empirical data point: a late DWT plus self-attention and a swarm-optimized head reaching roughly 98% accuracy on two standard skin-lesion benchmarks. The architecture description is readable, and the release of a public repository is a positive feature. However, the contribution is incremental and mostly empirical; the novelty lies in the specific combination of known components rather than in a fundamentally new method. The central claims are currently not supported because the statistical tables and protocol details contain serious problems that must be addressed.

major comments (5)
  1. [Tables 8–11 and Eqs. (34)–(35)] The ISIC-2017 significance tables (Tables 8–11) are numerically identical to the ISIC-2016 tables (Tables 3–6) except for a row label in Table 8. Because the 5-fold accuracies in Tables 2 and 7 differ between the two datasets, the two-sample t-statistics and p-values computed from Eqs. (34)–(35) cannot coincide to four decimal places. This invalidates the statistical support for the claimed ISIC-2017 improvements, and the tables need to be recomputed from the actual ISIC-2017 fold results or removed.
  2. [Table 1 and Results] The headline accuracy for DenseNet+Wavelet+MGTO on ISIC-2016 is reported as 98.87% in the Results section but as 97.87% in Table 1; this discrepancy must be resolved. In addition, the manuscript never states the train/test split for the Table 1 accuracies; the only split description (before Table 2) says 15% of the train data is used as validation and 65% as training, which is incomplete and does not specify the test set. Without a precise split, the reported accuracies cannot be reproduced.
  3. [Tables 2 and 7; text before Table 2] The claim that the k-fold results show "there is no sign of overfitting and underfitting" is unsupported because only validation-fold accuracies are presented; no training-set accuracy or train/test generalization gap is shown. The authors should either report training and test performance or remove the overfitting claim.
  4. [Results and Table 1] The best result is selected after comparing many model/optimizer combinations in Table 1, but no model-selection protocol is described. If the test set was used to choose the best combination, the reported accuracies are optimistically biased; the authors should describe how the validation set was used for selection and confirm that the final numbers are on an untouched test set.
  5. [Conclusion] The conclusion states that the method improves accuracy "by at least 1.1%" on ISIC-2016 and by "2.05%" on ISIC-2017 relative to not using swarm-based optimizers, but Table 1 does not show a single consistent baseline supporting these numbers; for example, DenseNet+Wavelet+Fox improves over DenseNet by 0.13 percentage points, while MobileNet+Wavelet+Fox improves over MobileNet by 3.13 percentage points. The quantitative gain claim should be recomputed and stated with the specific baseline used.
minor comments (6)
  1. [Conclusion] The heading "Conlusion" should be "Conclusion".
  2. [Figure 1] The caption for Figure 1 is inconsistent: it lists C1 and C2 for the Inception block but then says "B2: Inception Net Block"; the block labels should be corrected.
  3. [Figures 2–5] The legends in Figures 2–5 use the label "AGTO"; this should be "MGTO".
  4. [Table 8] Table 8 uses the row header "Xception+Wavelet+GWO"; this should be "Xception+Wavelet+IGWO" for consistency with Tables 3 and 10.
  5. [Methodology] The methodology says the swarm optimizers tune "filters size, kernel size" even though the trainable head after DWT and self-attention is a dense ANN; clarify whether the optimizers tune pretrained CNN hyperparameters or the ANN weights.
  6. [Literature review] The literature review cites references [5] and [11] as skin-cancer studies, but the titles indicate they concern sickle cell disease; please verify the citations or replace them with appropriate skin-cancer works.

Circularity Check

1 steps flagged · score 6.0 of 10

ISIC-2017 significance tables are verbatim copies of ISIC-2016 tables, making the statistical support for the second dataset a relabeled version of the first.

  1. renaming known result [Results section: Table 8 (ISIC-2017) and Table 3 (ISIC-2016), also Tables 9-11 vs 4-6]
    "Table 8. Xception statistic and pvalue (ISIC-2017) ... Xception 0.0, 1.0 -4.1328, 0.0032 -3.5555, 0.0074 -3.2377, 0.0119 -3.9350, 0.0043 Table 3. Xception statistic and pvalue (ISIC-2016) ... Xception 0.0, 1.0 -4.1328, 0.0032 -3.5555, 0.0074 -3.2377, 0.01191 -3.9350, 0.0043"

    Tables 8-11 (ISIC-2017) are numerically identical to Tables 3-6 (ISIC-2016) for every pairwise t-statistic and p-value, with only the header IGWO changed. The 5-fold accuracies in Table 7 (ISIC-2017) differ from Table 2 (ISIC-2016), and Eq. (34)-(35) define t as a deterministic function of those fold values. Identical t/p values cannot therefore be produced from the ISIC-2017 folds; the ISIC-2017 significance results are the ISIC-2016 results relabeled. The paper uses these tables to claim that wavelet/optimizer differences are significant on ISIC-2017, so that support reduces to the ISIC-2016 result, not to an independent computation on the advertised dataset.

full rationale

The paper's central accuracy claims are empirical measurements on ISIC-2016 and ISIC-2017; no mathematical derivation is present to be circular. The main load-bearing issue is that the ISIC-2017 significance tables (8-11) are numerically identical to the ISIC-2016 tables (3-6). Since Eq. (34)-(35) define the t-test as a function of the fold accuracies in Table 2 vs Table 7, identical values cannot be obtained from two different datasets; thus the ISIC-2017 significance evidence is the ISIC-2016 result relabeled as a new result, which is a self-referential/renamed form of support. The accuracy tables themselves are distinct between datasets and do not reduce to a fitted parameter, so the circularity is partial and confined to the statistical evidence.

Assumptions & free parameters 13 free parameters · 4 assumptions · 0 invented entities

The central claim depends on 8 hyperparameters fitted to the data plus 5 hand-set optimizer constants. No new physical or conceptual entity is introduced.

free parameters (13)
  • filters_size = optimized in [64,256]
    Number of filters in the convolutional layer, tuned by the swarm optimizers (Table 13).
  • kernel_size = optimized in [3,9]
    Kernel size, tuned by the swarm optimizers (Table 13).
  • learning_rate = optimized in [1e-5,1e-2]
    Learning rate for the ANN, tuned by the swarm optimizers (Table 13).
  • l2_reg = optimized in [1e-5,1e-2]
    L2 regularization coefficient, tuned by the swarm optimizers (Table 13).
  • l1_reg = optimized in [1e-5,1e-2]
    L1 regularization coefficient, tuned by the swarm optimizers (Table 13).
  • batch_size = optimized in [16,128]
    Batch size, tuned by the swarm optimizers (Table 13).
  • epochs = optimized in [10,100]
    Number of epochs, tuned by the swarm optimizers (Table 13).
  • att_reg_weight = optimized in [1e-5,1e-3]
    Regularization weight for the attention module, tuned by the swarm optimizers (Table 13).
  • MGTO_pp = 0.03
    Probability of transition in exploration phase, hand-set in Table 12.
  • FOX_c1 = 0.18
    Jump coefficient for northeast direction, hand-set in Table 12.
  • FOX_c2 = 0.82
    Jump coefficient for opposite direction, hand-set in Table 12.
  • IGWO_a_min = 0.02
    Lower bound of the decay parameter, hand-set in Table 12.
  • IGWO_a_max = 2.2
    Upper bound of the decay parameter, hand-set in Table 12.
assumptions (4)
  • domain assumption DWT provides a useful frequency decomposition of CNN feature maps
    The paper assumes splitting features into LL/LH/HL/HH sub-bands improves classification, without a theoretical or empirical ablation that isolates DWT alone.
  • domain assumption ISIC labels are correct and the dataset split is unbiased
    The entire evaluation rests on the correctness of public labels and on an unspecified split; no code or split file is provided.
  • ad hoc to paper Swarm optimizers converge to hyperparameters that generalize
    The optimizers are treated as reliable tuning tools; no comparison is made to simpler baselines such as random search or grid search.
  • domain assumption Pretrained ImageNet weights transfer to dermoscopy
    The paper uses DenseNet, Inception, Xception, and MobileNet without fine-tuning analysis or domain adaptation.

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Cite this review

Pith. "Pith review of Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers." pith.science (2026). https://pith.science/paper/YQHZKAJL

@misc{pith2026241200472,
  author       = {Pith},
  title        = {Pith review of: Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YQHZKAJL}},
  note         = {Machine review of arXiv:2412.00472}
}
read the original abstract

Skin cancer (SC) stands out as one of the most life-threatening forms of cancer, with its danger amplified if not diagnosed and treated promptly. Early intervention is critical, as it allows for more effective treatment approaches. In recent years, Deep Learning (DL) has emerged as a powerful tool in the early detection and skin cancer diagnosis (SCD). Although the DL seems promising for the diagnosis of skin cancer, still ample scope exists for improving model efficiency and accuracy. This paper proposes a novel approach to skin cancer detection, utilizing optimization techniques in conjunction with pre-trained networks and wavelet transformations. First, normalized images will undergo pre-trained networks such as Densenet-121, Inception, Xception, and MobileNet to extract hierarchical features from input images. After feature extraction, the feature maps are passed through a Discrete Wavelet Transform (DWT) layer to capture low and high-frequency components. Then the self-attention module is integrated to learn global dependencies between features and focus on the most relevant parts of the feature maps. The number of neurons and optimization of the weight vectors are performed using three new swarm-based optimization techniques, such as Modified Gorilla Troops Optimizer (MGTO), Improved Gray Wolf Optimization (IGWO), and Fox optimization algorithm. Evaluation results demonstrate that optimizing weight vectors using optimization algorithms can enhance diagnostic accuracy and make it a highly effective approach for SCD. The proposed method demonstrates substantial improvements in accuracy, achieving top rates of 98.11% with the MobileNet + Wavelet + FOX and DenseNet + Wavelet + Fox combination on the ISIC-2016 dataset and 97.95% with the Inception + Wavelet + MGTO combination on the ISIC-2017 dataset, which improves accuracy by at least 1% compared to other methods.

Figures

Figures reproduced from arXiv: 2412.00472 by the authors.

Figure 1
Figure 1. Structure of Proposed Method(The Alphabetical Abbreviations Stands for A1:MobileNetV1-DWT-SA Block, A2: Mobile Net Block, B1: DenseNet121-DWT-SA Block, B2: Dense Net Block, C1: InceptionNetV1-DWT-SA Block, B2: Inception Net Block, D: XceptionNet-DWT-SA Block) 5/21 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Bar plot for accuracy of different models on the classification of ISIC-2016 dataset. Accuracy F-measure Recall Precision Metrics 0.94 0.95 0.96 0.97 0.98 0.99 1.00 Scores Comparison of DenseNet Variants on ISIC-2017 Dataset DenseNet DenseNet+Wavelet DenseNet+Wavelet+IGWO DenseNet+Wavelet+Fox DenseNet+Wavelet+AGTO DenseNet Accuracy F-measure Recall Precision Metrics 0.94 0.95 0.96 0.97 0.98 0.99 1.00 Scores Comparis… view at source ↗
Figure 3
Figure 3. Bar plot for accuracy of different models on the classification of ISIC-2017 dataset. 12/21 [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Diagram for Model Performance Across 5 Folds on ISIC-2016 Dataset [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Diagram for Model Performance Across 5 Folds on ISIC-2017 Dataset. 15/21 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

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Reference graph

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Reviewed August 12, 2026 · model on record in the stance chip above.