{"id":"3cd3275b-6b0b-45de-814b-d4137acc4be6","arxiv_id":"2505.21597","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":2.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A custom CNN is reported to match ResNet50 accuracy on HAM10000 at 692K parameters and 30M FLOPs, but the paper's own layer table implies 25.8M parameters and hundreds of millions of FLOPs.","lead":"This paper proposes a small custom CNN for skin cancer classification on HAM10000 and claims it cuts parameters by 96.7% and FLOPs by 99.2% while staying within 0.022% accuracy of a transfer-learned ResNet50. The arithmetic in the paper's own architecture tables contradicts those numbers, so the central claim is not supported.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's central efficiency claim is contradicted by its own architecture table: the reported 692,807 parameters and 30.04M FLOPs cannot be derived from Table II, which sums to ~25.8M parameters and ~531M FLOPs under the paper's own formulas.","rationale":"The reader's REJECT verdict identifies the same load-bearing concern: the paper's headline efficiency numbers are irreconcilable with its own architecture table. My independent check confirms this. Summing Table II gives 25,785,159 parameters, not 692,807; applying the paper's Eqs. 10 and 11 to the same table gives about 531 million FLOPs, not 30.04 million. The Section IV.B text additionally swaps the parameter totals of the custom CNN and the ResNet50 baseline, and the 0.022% accuracy-deviation claim is inconsistent with the 89.08% versus 87.05% results. These are internal numerical inconsistencies, not merely disagreements with external consensus, so they invalidate the central claim. The paper does provide a clear architecture and explicit formulas, which makes the contradiction directly checkable; no code, data splits, seeds, or training details are provided, so there is no external way to recover the claimed numbers. Because the central contribution is an efficiency comparison and that comparison cannot be reproduced from the manuscript, the REJECT verdict stands unchanged.","tokens_in":8881,"tokens_out":3766,"duration_ms":38610,"concrete_test":"Independently sum the parameter column of Table II exactly as printed: 896 + 18,496 + 73,856 + 25,690,112 + 1,799 = 25,785,159. Then compute FLOPs for those same layers using Eqs. 10 and 11: Conv2D layers contribute 43,352,064 + 231,211,008 + 231,211,008 FLOPs and the dense layers contribute 25,690,112 + 1,792 FLOPs, for a total of 531,465,984 FLOPs. Compare these sums with the claimed 692,807 parameters and 30.04 million FLOPs in Tables III and IV. If the sums do not match, recompute every derived percentage (96.7% reduction, 99.2% FLOP reduction, 13,216.76% increase) using the corrected numbers, and also resolve the swapped labels in Section IV.B. This arithmetic check alone settles whether the central efficiency claim can be true for the architecture presented.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that the custom CNN has 692,807 parameters and 30.04 million FLOPs, achieving a 96.7% parameter reduction and 99.2% FLOP reduction versus ResNet50-based transfer learning—is the load-bearing result of the paper. However, it is not supported by the paper's own layer-by-layer specification. Table II, which is explicitly described as 'the details of the proposed custom CNN architecture,' lists layers whose parameter counts sum to 896 + 18,496 + 73,856 + 25,690,112 + 1,799 = 25,785,159 parameters, not 692,807. Using the paper's own FLOP formulas in Section III-G (Eqs. 10 and 11), the same Table II layers yield approximately 531 million FLOPs (43.35M + 231.21M + 231.21M + 25.69M + 0.002M), roughly 17.7 times the stated 30.04 million. Moreover, Section IV.B swaps the labels: it says the baseline ResNet50+TL 'lowers the total parameters to 692,807' while 'our custom CNN ... includes just 23.66 million parameters,' directly contradicting Table III and the Abstract. Regardless of which label is correct, the claimed efficiency comparison cannot be reconstructed from the reported architecture. A secondary issue reinforces the problem: Table IV reports an accuracy deviation of only 0.022% between 89.08% and 87.05%, but the actual difference is 2.03 percentage points; that is not a 'minimal accuracy trade-off' in the sense claimed. Together these inconsistencies invalidate the headline parameter, FLOP, and accuracy-deviation figures. The conceptual goal of a lightweight skin-cancer classifier is reasonable, but this manuscript does not provide a verifiable model or reproducible numbers for it.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a custom CNN for seven-class skin-lesion classification on HAM10000 and compares it with ResNet50-based transfer learning. The central claim is that the custom CNN reduces parameters from 23.9 million to 692,807 (a 96.7% reduction) and FLOPs from 4.00 billion to 30.04 million (a 99.2% reduction) while staying within 0.022% accuracy of the transfer-learning baseline. These figures appear in the Abstract, Section V, Table III, and Table IV. The manuscript also claims a 60% training-time reduction and a 48.5% inference speedup, and asserts that the ResNet50 baseline achieves 89.08% accuracy versus the custom CNN's 87.05%.","tokens_in":9365,"tokens_out":5952,"duration_ms":62641,"significance":"The practical goal of a lightweight, deployable skin-cancer classifier is timely, and the emphasis on FLOPs as a comparison metric is appropriate for edge-deployment arguments. If the reported efficiency figures were reproducible, the paper would provide a useful benchmark. However, the central quantitative claims cannot be reconstructed from the paper's own architecture description and results, so the significance of the contribution as written is not established. The manuscript also provides no code, training hyperparameters, data split, or timing methodology, which further limits verification of the efficiency claims.","major_comments":[{"comment":"The parameter total claimed for the custom CNN is not supported by the architecture table. Table II lists five parameter-bearing layers: Conv2D with 896, Conv2D with 18,496, Conv2D with 73,856, Dense with 25,690,112, and Dense with 1,799 parameters. These sum to 25,785,159 parameters, not 692,807. Consequently, the 96.7% parameter reduction claimed in the Abstract, Section V, and Table III cannot be derived from the described architecture.","section":"Table II and Abstract/Conclusion"},{"comment":"The reported 30.04 million FLOPs is inconsistent with the paper's own FLOP formulas applied to Table II. Using Eq. (10) for the three convolutional layers gives 43.35M, 231.21M, and 231.21M FLOPs, and using Eq. (11) for the two dense layers gives 25.69M and 0.002M FLOPs, for a total of approximately 531.5M FLOPs. This is about 17.7 times the stated 30.04M and already uses the paper's favorable single-multiply counting. The 99.2% FLOP reduction claim therefore cannot be reproduced from the reported architecture.","section":"Section III-G Eqs. (10)-(11) and Table II"},{"comment":"Section IV.B contradicts Table III and the Abstract by swapping the labels of the two models. The text states that 'ResNet50 + TL lowers the total parameters to 692,807' while 'our custom CNN ... includes just 23.66 million parameters,' but Table III and the Abstract assign 23,661,703 parameters to ResNet50+TL and 692,807 to the custom CNN. This is an internal inconsistency in a load-bearing comparison, and the reader cannot determine which assignment is correct.","section":"Section IV.B and Table III"},{"comment":"The claimed 'accuracy deviation of less than 0.022%' is arithmetically wrong. Table IV reports accuracies of 87.05% and 89.08%, whose difference is 2.03 percentage points, not 0.022 percentage points. The Abstract and Section IV.C repeat the 0.022% figure, understating the accuracy gap by roughly two orders of magnitude. This directly undermines the asserted 'minimal accuracy trade-off.'","section":"Table IV and Abstract"},{"comment":"The paper states that binary cross-entropy loss is used with labels 0/1 for benign versus malignant, but Table II specifies a seven-unit output with softmax and the dataset is described as having seven lesion classes. The training objective is therefore not specified consistently, which makes the reported accuracies difficult to interpret. This should be resolved by stating whether a seven-class categorical loss or a binarized melanoma-versus-rest task was used.","section":"Section III.D and Table II"},{"comment":"The Conclusion claims 'over a 60% reduction in training time and a 48.5% speedup in inference,' but no timing experiments, hardware description, or measurement protocol appear anywhere in the manuscript. This is an additional efficiency claim that is load-bearing for the 'computationally efficient' characterization and is currently unsupported.","section":"Section V"}],"minor_comments":[{"comment":"The Adam update rule is written incorrectly: the denominator should be the square root of the second-moment estimate plus epsilon, and the numerator should be the first-moment estimate, not v_t divided by sqrt(m_t).","section":"Section III.E Eq. (5)"},{"comment":"The paper says the proposed system has an accuracy of 87.08% in Section II, but Table IV reports 87.05%. This minor inconsistency should be corrected.","section":"Section II and Table IV"},{"comment":"Reference [30] is cited to support 'sequential correlations' as a data-augmentation technique, but the cited paper appears to be about order dependency in sequence correlation and does not obviously describe image augmentation. The authors should remove or justify this citation.","section":"Section III-B and Reference [30]"},{"comment":"The accuracy and loss curves, confusion matrix, and ROC curves are presented only for the ResNet50 transfer-learning model; no training curves or confusion matrix are shown for the proposed custom CNN, so the reader cannot verify its generalization behavior.","section":"Section IV"},{"comment":"The terminology 'FLOP' and 'FLOPs' is used inconsistently; the text defines FLOPs as 'Floating Point Operations per Second,' but the tables and equations use FLOPs to mean the total number of floating-point operations. This should be unified.","section":"Section III-G"},{"comment":"The column labeled 'Param. Red.' lists 'Yes' for the proposed method without a value or a definition of how parameter reduction is measured; the comparison would be clearer if the actual reduction percentage or parameter count were given.","section":"Table I"}],"recommendation":"reject","confidential_remarks":"The rejection is based on internal numerical contradictions that invalidate the central efficiency and accuracy claims. The only citation-pattern concern is the peripheral self-citation [30], which does not support the statement it is attached to. These issues would require substantially reworking the experimental reporting and the core comparisons, so a major revision is not an adequate remedy at this stage."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe one thing you should know: the headline numbers in this paper are not supported by its own architecture table. Summing Table II gives roughly 25.8 million parameters, not the claimed 692,807. Applying the paper's own FLOP formulas to those layers gives around 531 million FLOPs, not 30.04 million. The accuracy deviation is also reported as 0.022% when the actual gap between 89.08% and 87.05% is 2.03 percentage points. These aren't rounding errors; they invalidate every efficiency claim in the abstract and conclusion.\n\nThe paper does address a real problem. Lightweight skin lesion classification for mobile and edge deployment matters, and comparing a small CNN against ResNet50 transfer learning on HAM10000 is a reasonable exercise. That part is fine as far as it goes, but the custom CNN is a generic stack of conv, pooling, flatten, dense, and dropout. There is no new architecture, training scheme, or loss. The only thing that could have made this interesting is the efficiency numbers, and those don't hold.\n\nThe internal contradictions go deeper. Section IV.B swaps the labels: it says the baseline ResNet50+TL 'lowers the total parameters to 692,807' while the custom CNN 'includes just 23.66 million parameters,' the opposite of Table III and the abstract. There is also no code, no data split details, no seeds, and no training hyperparameters. The results are not independently reproducible. The self-citation [30] is peripheral; the issue is arithmetic, not citations.\n\nThis is a load-bearing flaw, not a minor omission. The idea of an efficient skin cancer classifier is worth pursuing, but this manuscript does not provide a verifiable implementation. If the authors corrected the arithmetic, reported honest FLOPs, and released code, there might be a modest empirical contribution. As it stands, the paper should not go to peer review in anything close to this form. I'd desk reject it and tell the authors to redo the numbers and rerun the experiments with full transparency.","headline":"The paper's central efficiency claim collapses under its own architecture table; desk reject unless the authors redo the arithmetic and release code.","tokens_in":9858,"tokens_out":3261,"would_cite":false,"duration_ms":30026,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A custom CNN with 692,807 parameters classifies skin lesions on HAM10000 within the reported accuracy margin of a ResNet50 transfer-learning baseline while using far fewer FLOPs.","keywords":["skin cancer classification","convolutional neural network","transfer learning","HAM10000","FLOP reduction","parameter efficiency","melanoma detection","edge deployment"],"falsifier":"Sum the parameters of each layer in Table II using standard formulas and apply the paper's FLOP equations (10) and (11) to the same layer shapes; if the resulting totals are not 692,807 and 30.04 million, the headline reductions do not describe the printed architecture. Recomputing the accuracy difference from the published confusion matrix would similarly settle whether the gap is below 0.022%.","tokens_in":8664,"feed_emoji":"🩺","tokens_out":8247,"duration_ms":85942,"temperature":0.7,"pith_summary":"This paper argues that a deliberately small convolutional network can give nearly the same skin-lesion classification accuracy as a ResNet50 transfer-learning baseline while consuming only a tiny fraction of the compute. On the HAM10000 dataset it reports 87.05% accuracy for the custom CNN versus 89.08% for ResNet50 with transfer learning, a difference the paper describes as less than 0.022%. The efficiency claims are a 96.7% parameter reduction (692,807 versus 23.9 million) and a 99.2% FLOP reduction (30.04 million versus 4.00 billion). The authors' point is that for deployment on mobile and edge devices, this trade-off may be preferable to a heavyweight model.","feed_headline":"A 692K-parameter CNN matches ResNet50's skin cancer accuracy","feed_subtitle":"30 million FLOPs versus 4 billion, with a reported accuracy gap of 0.022%.","key_machinery":"The central object is the proposed custom CNN architecture of Table II: three Conv2D-MaxPool blocks with 32, 64, and 128 filters using $3\\times3$ kernels, followed by a Flatten layer, a 256-unit Dense layer with 50% dropout, and a 7-unit softmax output. The measurement machinery is the pair of FLOP formulas in the paper, one for convolutional layers and one for dense layers, which the authors use to compare the custom network against the ResNet50 transfer-learning baseline. The residual connections in ResNet50 are explained as the contrast: they preserve accuracy but add the computational cost the paper wants to avoid.","core_discovery":"The central discovery, as the authors present it, is that a purpose-built CNN with only one fully connected hidden layer can match most of the accuracy of a transfer-learned ResNet50 on the seven-class HAM10000 lesion dataset. The paper reports 87.05% accuracy and 30.04 million FLOPs for the custom network, versus 89.08% accuracy and 4.00 billion FLOPs for ResNet50, a gap it states as less than 0.022%. It also reports a 96.7% parameter reduction and over 60% less training time, and concludes that such lightweight models are viable for mobile and edge diagnostics.","pith_inferences":["Because the paper compares against a single transfer-learning baseline, its conclusion is specific to HAM10000; a multi-site dermoscopy benchmark would test whether the reported accuracy gap persists with more lesion diversity.","A from-scratch version of the custom CNN on the same data would isolate whether the gap comes from model capacity or from ImageNet initialization, a distinction the paper does not examine.","The percentage reductions depend on which ResNet50 head is used; comparing against a standard pretrained ResNet50 with no custom head would show how much of the saving is architectural rather than a transfer-learning choice.","Recomputing the accuracy difference as a relative change rather than an absolute gap would clarify whether the 0.022% wording is a rounding convention or a different metric than the table values suggest."],"forward_implications":["Devices with tight memory and power budgets could run per-image screening at 30.04 million FLOPs while staying close to the baseline's reported accuracy.","The reported 96.7% parameter cut and 99.2% FLOP cut would shrink model size, energy per inference, and latency, which are the constraints that block edge deployment.","A clinic that values throughput over a fraction of a percent of accuracy could use the custom CNN as the primary model and reserve ResNet50 for ambiguous cases.","The authors' planned quantization and pruning could drive computational cost down further, making the trade-off even more favorable for embedded hardware.","The comparison is specific to the seven HAM10000 classes, so applying the same architecture to a different lesion taxonomy would require retraining and re-validation."],"supporting_citations":[{"why":"Supplies the HAM10000 dermoscopic images and labels used to train and test both models.","marker":"[29]"},{"why":"Establishes FLOPs as the computational-cost metric the paper uses for the efficiency comparison.","marker":"[14]"},{"why":"Provides the FLOP-aware optimization context that motivates reducing FLOPs while maintaining accuracy.","marker":"[15]"},{"why":"Links FLOP counts to energy consumption, supporting the paper's claim that lower FLOPs aid real-world deployment.","marker":"[27]"},{"why":"Documents the parameter and accuracy trade-offs of transfer learning, framing the ResNet50 baseline.","marker":"[13]"}],"fun_headline_variants":["Skin cancer CNN: 96.7% fewer parameters, accuracy dip under 0.022%","Lightweight CNN matches ResNet50 on skin cancer, 133x fewer FLOPs","692K-parameter CNN nearly matches ResNet50 skin cancer accuracy","Edge CNN for skin cancer: 30M FLOPs, 692K params, similar accuracy","Skin cancer model: 30M FLOPs vs ResNet50's 4B, accuracy gap tiny"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The central comparison rests on the totals in Tables III and IV being measured consistently for the same models: 692,807 parameters, 30.04 million FLOPs, and a reported accuracy gap of less than 0.022% for the custom CNN; if those totals do not correspond to the described networks, the efficiency and minimal-trade-off claims are not anchored.","fun_headline_variants_meta":{"raw":{"variants":["Skin cancer CNN: 96.7% fewer parameters, accuracy dip under 0.022%","Lightweight CNN matches ResNet50 on skin cancer, 133x fewer FLOPs","692K-parameter CNN nearly matches ResNet50 skin cancer accuracy","Edge CNN for skin cancer: 30M FLOPs, 692K params, similar accuracy","Skin cancer model: 30M FLOPs vs ResNet50's 4B, accuracy gap tiny"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001243,"raw_usage":{"total_tokens":5076,"prompt_tokens":898,"completion_tokens":4178,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":514,"completion_tokens_details":{"reasoning_tokens":4059}},"tokens_in":514,"tokens_out":4178,"duration_ms":28476,"temperature":1.0,"reasoning_tokens":4059,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T13:27:49.968290+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Sum the parameters of each layer in Table II using standard formulas and apply the paper's FLOP equations (10) and (11) to the same layer shapes; if the resulting totals are not 692,807 and 30.04 million, the headline reductions do not describe the printed architecture. Recomputing the accuracy difference from the published confusion matrix would similarly settle whether the gap is below 0.022%.","supporting_citations":[{"cited_title":"The HAM10000 dataset, a large collection of multi- source dermatoscopic images of common pigmented skin lesions,","cited_arxiv_id":null,"evidence_quote":"Supplies the HAM10000 dermoscopic images and labels used to train and test both models."},{"cited_title":"Run, don’t walk: Chasing higher flops for faster neural networks,","cited_arxiv_id":null,"evidence_quote":"Establishes FLOPs as the computational-cost metric the paper uses for the efficiency comparison."},{"cited_title":"FALCON: FLOP-aware combinatorial optimization for neural network pruning,","cited_arxiv_id":null,"evidence_quote":"Provides the FLOP-aware optimization context that motivates reducing FLOPs while maintaining accuracy."},{"cited_title":"Analysis and prediction of energy consumption in neural networks based on machine learning,","cited_arxiv_id":null,"evidence_quote":"Links FLOP counts to energy consumption, supporting the paper's claim that lower FLOPs aid real-world deployment."},{"cited_title":"Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the cnn-based deep learning models,","cited_arxiv_id":null,"evidence_quote":"Documents the parameter and accuracy trade-offs of transfer learning, framing the ResNet50 baseline."}],"review_version":1}