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REVIEW 5 major objections 6 minor 44 references

Classification based deep learning models for lung cancer and disease using medical images

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper proposes ResNet+, a ResNet variant that inserts CBAM attention into bottleneck blocks and adopts ResNet-D downsampling, and reports that it outperforms ResNet50/101 on five public lung imaging datasets, with best results of…

desk verdict The paper's central empirical claim is invalidated by impossible metric combinations in Table II, despite some useful ablations and code. read the letter →

arxiv 2507.01279 v1 pith:QY6YYVCH submitted 2025-07-02 eess.IV cs.CV

Ahmad Chaddad , Jihao Peng , Yihang Wu This is my paper
classification eess.IVcs.CV
keywords lungcancerdeeplearningResNetCBAMNet-DmedicalimageclassificationCTimaginghistopathologyimages
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

The paper tries to show that a small architectural change to a standard image-classification network can make lung-cancer screening from medical images both more accurate and cheaper. It adds a channel-and-spatial attention module (CBAM) to every bottleneck block of ResNet50/101 and replaces the downsampling path with ResNet-D's average-pooling shortcuts, calling the result ResNet+. Across five public datasets spanning histopathology slides, CT slices, and chest X-rays, the modified model is reported to beat the unmodified ResNet series on accuracy and on the F1 score, a combined precision-and-recall measure, while using less training time on the two lung-cancer sets. If those numbers hold, the recipe of attention plus lossless downsampling would be a low-cost upgrade for image-based cancer classifiers.

What carries the argument

The central mechanism is the ResNet+ block: a ResNet bottleneck whose shortcut path uses ResNet-D's average-pooling-then-1-by-1-convolution downsampling, and whose feature map passes through a Convolutional Block Attention Module (CBAM) before being added to the shortcut. ResNet-D alters the stem and shortcut so that stride-2 downsampling does not discard feature information, while CBAM—channel attention built from squeeze-and-excitation on pooled features, then spatial attention built from a 7-by-7 convolution on concatenated channel-pooled maps—reweights features toward informative regions. The paper's claim is that these two additions together, rather than either one alone, produce the accuracy gains and the training-time savings.

What would settle it

Reproduce the LCC confusion matrix: the test set is balanced at 1,000 images per class, so an accuracy of 99.99% would mean essentially every one of the 5,000 images is correct and the macro F1 would sit near 99%, not at the reported 79.85%. Recomputing accuracy, precision, recall, and F1 from the same predictions—and checking that the 110 IQ-OTH/NCCD patients are split at the patient level—would settle whether the reported gains are real.

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Extended reading notes

Core claim

The paper's central claim is that equipping ResNet50 and ResNet101 with ResNet-D's downsampling design and a CBAM attention module—the combination it calls ResNet+—improves classification accuracy across five public lung-cancer and lung-disease image datasets compared with the unmodified ResNet series. On the paper's reported numbers, the best variant reaches 98.14% accuracy and F1 on LC25000 histopathology images, 99.25% accuracy and 99.13% F1 on IQ-OTH/NCCD CT slices, and about two percentage points of accuracy gain on the five-class LCC set, with ResNet50+ also reducing training time on the two lung-cancer datasets relative to ResNet50. The paper further claims that ablations show CBAM and ResNet-D are complementary, and that the resulting model transfers to a CT-plus-pathology multimodal task and to skin-lesion classification.

Load-bearing premise

The load-bearing premise is that the reported test metrics are genuine, meaning they are computed from the same predictions and no images of the same patient appear in both the training and test sets; the LCC row of Table II, which pairs 99.99% accuracy with 79.85% F1 on a balanced test set, is not compatible with that premise.

Editorial extensions

If this is right

  • Applying ResNet-D plus CBAM to an existing ResNet classifier could raise accuracy on other medical image tasks without changing the data pipeline or the parameter count much.
  • The ablation results imply that attention alone or downsampling alone gives smaller gains, so future architecture search should treat the two modifications as a paired recipe.
  • The reported training-time savings mean that on small CT and histopathology sets, retraining the improved model would be faster, making higher accuracy available at lower compute cost.
  • Data augmentation for underrepresented classes appears sufficient to reach high accuracy on the imbalanced IQ-OTH/NCCD set, pointing to a reusable recipe for other small medical datasets.
  • The multimodal CT-plus-pathology experiment suggests the same architecture can fuse different imaging modalities without a redesign.

Reading between the lines

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

  • The paper does not report a patient-level split for IQ-OTH/NCCD; if the 99.25% accuracy persisted under such a split, that would be strong evidence of generalization, whereas image-level splits can overstate performance on CT slices from the same patient.
  • The computational-cost claim is tied to one GPU and to the time until the best validation model; a fairer extension would report FLOPs or energy across hardware, since the ChestXray and large CT results do not show consistent savings.
  • A direct recomputation of all metrics from one confusion matrix for the LCC test set would settle the paper's central uncertainty: either accuracy and F1 reconcile, or a reporting error hides the true comparison.
  • Because CBAM is a generic attention module, the same architecture could be tested on other imbalanced pathology datasets; a reasonable extension is to compare training-from-scratch against fine-tuning pre-trained weights.
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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 ResNet+, a modification of ResNet50 and ResNet101 that combines ResNet-D downsampling changes with Convolutional Block Attention Modules (CBAM), and evaluates it on five public lung image datasets: LC25000, IQ-OTH/NCCD, LCC, ChestXray, and COVIDx-CT. The authors report high test accuracies (e.g., 98.14% on LC25000, 99.25% on IQ-OTH/NCCD), claim reduced computational cost relative to the original ResNet series, present ablations of CBAM and ResNet-D components, and describe a 'multimodal' experiment that pools LC25000 and IQ-OTH/NCCD images. Public code is linked in the abstract.

Significance. If the reported results were valid, the paper would provide a simple architectural recipe—ResNet-D plus CBAM—that improves multiclass lung-image classification across several public datasets and lowers training time on some of them. The paper includes useful components: publicly available code, multiple public datasets, and a systematic ablation of CBAM and ResNet-D. However, the empirical foundation contains internally inconsistent metrics and a probable data-splitting problem, so the significance of the contribution cannot be assessed until those issues are resolved. There is no circularity in the central claim, since the results are external benchmark comparisons rather than derivations from the data.

major comments (5)
  1. [Table II, Section III-A] The LCC ResNet50+ row reports ACC=99.99%, PRE=81.85%, REC=84.58%, F1=79.85% on a test set that Section III-A states is balanced with 5 classes x 1000 images. An accuracy of 99.99% would require 4999.5 correct predictions out of 5000, which is impossible; if rounded from 4999/5000 the value would be 99.98%, not 99.99%. Moreover, with at most one misclassification, macro precision, recall, and F1 would all be near 99.98%, not the 80% range. The same PRE/REC/F1 values are then repeated exactly in the COVIDxCT rows of Table II (e.g., ResNet50+ and ResNet101+), despite COVIDxCT having a differently sized, imbalanced three-class test set. These internal inconsistencies invalidate the LCC and COVIDxCT performance claims in Section IV-B.
  2. [Section III-A (IQ-OTH/NCCD)] The IQ-OTH/NCCD dataset is described as CT slices derived from 110 patients, yet the paper reports using 1336 images split into 1070/133/133 without any patient-level split. Since a patient can contribute multiple slices, an image-level random split very likely places slices of the same patient in both training and test sets, causing patient-identity leakage and inflating the 99.25% accuracy highlighted in the abstract. The authors need to specify how the split was performed at the patient level, or demonstrate that no patient appears in both sets; without this, the IQ-OTH/NCCD result is not interpretable.
  3. [Abstract, Section IV-B, Tables III-IV] The abstract's unqualified claim that ResNet+ 'saved computational cost compared to the original ResNet series' is contradicted by the paper's own measurements. Table IV shows that ResNet50+ and ResNet101+ have higher per-sample inference latency than the corresponding baselines on every dataset (e.g., 7.63±0.92 ms vs 6.52±0.78 ms on IQ-OTH/NCCD; 11.85±1.54 ms vs 11.07±1.33 ms on LC25000). Table III also shows greater training time for ChestXray (154.16 vs 147.39 min for ResNet50+ vs ResNet50). The cost savings appear only for some training runs on lung-cancer datasets, so the claim should be restricted to that setting or removed from the abstract.
  4. [Section IV-B (Multi-modality data, Table VI)] The experiment described as 'multimodal' simply pools LC25000 histopathology images and IQ-OTH/NCCD CT images into one training set. No modality encoding, separate branch, modality-conditional mechanism, or modality label is provided, so a single CNN trained on the combined images cannot exploit the fact that the inputs come from different modalities. This does not support the paper's stated contribution of combining different modalities; it is at best a multi-dataset pooling experiment. The claim needs to be substantially reframed or tested with an architecture that actually models modality identity.
  5. [Abstract, Section V] The abstract's statement that 'The proposed model outperformed the baseline models on publicly available datasets' is not supported by the data presented. In Table II, ResNet50+ has lower accuracy than ResNet50 on COVIDxCT (78.45% vs 80.58%), and in Table VIII ResNet50+ has lower accuracy than ResNet50 on ISIC2018 (67.98% vs 68.12%). Section V itself acknowledges that ResNet50+ performed worse than its standard model on a large dataset. The conclusions should be qualified to state which datasets and metrics support the claimed improvement.
minor comments (6)
  1. [Eq. (6)] The F1-score formula is written as '2 × TP / 2 × FP + FN', which omits TP in the denominator; the correct expression is 2*TP / (2*TP + FP + FN).
  2. [Section III-A, Table I, Abstract] The ChestXray dataset size is inconsistent: Section III-A says 5863 images, while Table I sums to 5216+16+624=5856 and the abstract reports n=5856. Please unify these counts.
  3. [Section IV-B] The text says the ResNet+ models give '~4% improvements' in ACC on ChestXray, but the differences in Table II are about 3.0 percentage points (87.98 vs 84.94 and 83.01 vs 79.97), not 4%.
  4. [Throughout] The dataset name is written inconsistently as IQ-OTHNCDD, IQ-OTH/NCCD, and IQ-OCTNCCD; please choose one spelling and use it consistently.
  5. [Figure 4 caption] The caption says 'Scc' stands for 'small cell lung cancer' in the LC25000 confusion matrix, but in the paper 'Scc' denotes squamous cell carcinoma; please correct the caption.
  6. [Section IV-A] The training description gives a batch size, optimizer, EMA decay, and 200 epochs, but no random seeds or exact data-split procedure are reported; adding this information would improve reproducibility beyond the public code link.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ResNet+ is an empirical combination of externally published components benchmarked on public datasets; self-citations are discussion-level and not load-bearing.

full rationale

The paper's central claim is an empirical performance comparison: ResNet+ is constructed by combining two externally published techniques, ResNet-D [13] and CBAM [14], and then evaluated on five public datasets with reported held-out test metrics. There is no derivation chain in which an output quantity is defined in terms of the quantity it is said to predict, and no fitted parameter is renamed as a prediction. The architecture improvements are adopted from independent prior work, not from a self-citation chain that forces the conclusion. The only self-citations are references [43] and [44] in the discussion, where federated learning and explainable AI are mentioned as future directions; these are not used to justify the accuracy, F1, or computational-cost claims. The serious problems in the paper, such as the internally impossible accuracy/precision/recall/F1 combinations in Table II and the reuse of exact metric values across LCC and COVIDxCT rows, undermine the reliability of the reported numbers, but they are evidence of incorrect or inconsistent evaluation rather than circular reasoning. Because the empirical claims are compared against external benchmarks and the model is not derived from its own outputs, no circular step can be exhibited.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim is empirical and rests on dataset quality, split integrity, and correct metric computation. There is no theoretical derivation, so the ledger records data-assumption axioms and one hand-tuned hyperparameter.

free parameters (1)
  • Initial learning rate = 0.01
    Chosen after tuning experiments (Figure 6) showing that 0.1 and 0.00001 degrade accuracy; the model's claim is not derived from this, but it is a hand-selected hyperparameter.
assumptions (3)
  • domain assumption Public dataset labels and train/test splits are correct and representative of lung cancer and disease populations.
    Section III-A describes the five datasets but does not verify label quality or split integrity.
  • domain assumption No patient overlap between training and test splits.
    Section III-A gives no patient-level split procedure for IQ-OTH/NCCD or COVIDx-CT; slices from the same patient could appear in both splits, inflating accuracy.
  • ad hoc to paper The combined 'multimodal' dataset can be treated as a single image domain without modality encoding.
    Section IV-B, 'Multi-modality data', concatenates LC25000 histopathology and IQ-OTH/NCCD CT images into one training set, implicitly assuming a single model can map both modalities to the same label space without extra information.

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Cite this review

Pith. "Pith review of Classification based deep learning models for lung cancer and disease using medical images." pith.science (2026). https://pith.science/paper/QY6YYVCH

@misc{pith2026250701279,
  author       = {Pith},
  title        = {Pith review of: Classification based deep learning models for lung cancer and disease using medical images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QY6YYVCH}},
  note         = {Machine review of arXiv:2507.01279}
}
abstract

The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate the ResNet-D module, a variant designed to enhance feature extraction capabilities by modifying the downsampling layers, into the traditional ResNet model. Furthermore, a convolutional attention module was incorporated into the bottleneck layers to enhance model generalization by allowing the network to focus on relevant regions of the input images. We evaluated the proposed model using five public datasets, comprising lung cancer (LC2500 $n$=3183, IQ-OTH/NCCD $n$=1336, and LCC $n$=25000 images) and lung disease (ChestXray $n$=5856, and COVIDx-CT $n$=425024 images). To address class imbalance, we used data augmentation techniques to artificially increase the representation of underrepresented classes in the training dataset. The experimental results show that ResNet+ model demonstrated remarkable accuracy/F1, reaching 98.14/98.14\% on the LC25000 dataset and 99.25/99.13\% on the IQ-OTH/NCCD dataset. Furthermore, the ResNet+ model saved computational cost compared to the original ResNet series in predicting lung cancer images. The proposed model outperformed the baseline models on publicly available datasets, achieving better performance metrics. Our codes are publicly available at https://github.com/AIPMLab/Graduation-2024/tree/main/Peng.

Figures

Figures reproduced from arXiv: 2507.01279 by the authors.

Figure 1
Figure 1. Diagram illustrating the workflow for classifying lung cancer images. It covers image acquisition, data augmentation, [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Example images derived from LC25000, IQ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Structure of the classifier model includes stages with [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Confusion matrix of the CNN models using IQ-OTCNCCD ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Examples of DCA and ROC curves for baselines and our model ( [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Accuracy (%) of test samples derived from five datasets [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.