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REVIEW 4 major objections 5 minor 22 references

Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A hybrid ResNet101-plus-custom-CNN architecture classifies kidney CT images into normal, stone, cyst, and tumor categories, reporting 100% test accuracy on a 3,734-image test set.

desk verdict The paper's 100% test accuracy is not credible because the test set is used for validation; the rest is a routine feature-fusion baseline on a public dataset. read the letter →

arxiv 2502.04367 v1 pith:CKJ3GMXX submitted 2025-02-05 eess.IV cs.CVcs.LG

Kiran Sharma , Ziya Uddin , Adarsh Wadal , Dhruv Gupta This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords kidneyCTclassificationhybridCNNResNet101featurefusionstonescyststumorsdataaugmentation
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 establish that fusing a pre-trained ResNet101 with a custom CNN through feature fusion can classify kidney CT images into four clinically distinct categories with perfect accuracy on its test set. If true, an automated system could flag stones, cysts, and tumors from CT scans with no false positives or false negatives in this dataset, and do so with a faster testing time than ResNet101 alone. The architecture splits the task into normal-vs-stone and cyst-vs-tumor branches inside ResNet101, then merges those features with a custom CNN stream via an IntersectFeatures operation that keeps high-similarity features. The authors report 99.73% training accuracy and 100% test accuracy, precision, recall, and F1 on a 3,734-image test drawn from a 12,446-image public CT dataset expanded by augmentation to 27,035 images.

What carries the argument

The load-bearing component is feature fusion implemented by an IntersectFeatures operation, which combines the features extracted from a pre-trained ResNet101 and a custom CNN and keeps the features with higher similarity between the two streams. The fused representation is then flattened and passed through dense layers with dropout and a softmax head that outputs probabilities for normal, stone, cyst, and tumor. A second design choice is the dual-branch split of ResNet101, with one branch specializing in normal-vs-stone discrimination and the other in cyst-vs-tumor discrimination, so that the features merged into the final classifier encode task-specific knowledge from both subtasks.

What would settle it

Rerun the pipeline with the 70/30 split made before any augmentation, augment only the training portion, and measure accuracy on the untouched held-out images; if accuracy falls below 100% or errors appear on stones or tumors, the reported perfect score depended on test contamination or validation leakage. A second check is to test the trained model on an external kidney CT dataset with the same four labels.

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Extended reading notes

Core claim

The central claim is that the hybrid model assigns every image in its 3,734-image test set to the correct class, achieving 100% accuracy, precision, recall, and F1, while ResNet101 alone reaches 99.81% accuracy and the base CNN only 77.39%. In the confusion matrix the hybrid model has zero misclassifications, including for stones, a class that ResNet101 confuses with cysts in four cases. The authors interpret this as evidence that fusing the two feature streams filters out common or noisy features and separates class clusters that overlap in the ResNet101 feature space, as illustrated by PCA plots showing cleaner separation for the hybrid model.

Load-bearing premise

The perfect test score rests on the test images being truly unseen during training and model selection, but the paper does not state clearly that augmentation happened only after the 70/30 split and it uses 20% of the test set for validation.

Editorial extensions

If this is right

  • A fused two-stream CNN can separate all four kidney classes in this dataset, including the stone/tumor pairs that ResNet101 alone confuses.
  • The 100% per-class recall means the model misses no tumors or stones in the 3,734-image test set, which is the outcome that matters for screening.
  • The model reaches near-perfect accuracy within five epochs, so the fused representation is learned quickly on well-annotated CT data.
  • Testing time of 23 seconds for 3,734 images is shorter than ResNet101's 31 seconds, supporting use in high-throughput reading.
  • The dual-branch design shows that encoding clinical subtasks explicitly can improve a transfer-learning baseline on imbalanced medical image classes.

Reading between the lines

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

  • Beyond the paper: the perfect score is best understood as dataset-level, since the paper does not fully specify whether augmentation occurred before or after the 70/30 split; applying the same pipeline to an external CT dataset would test whether the fused features generalize.
  • Beyond the paper: the dual-branch design encodes the clinical prior that normal-vs-stone and cyst-vs-tumor are separable subtasks; ablating one branch would reveal how much of the gain comes from this split rather than from feature fusion.
  • Beyond the paper: the IntersectFeatures operation, which retains high-similarity features from both streams, resembles a consensus filter and could transfer to other small-data medical imaging tasks if it is the active ingredient.
  • Beyond the paper: the unusually fast convergence and perfect validation scores from epoch 3 onward suggest that the reported accuracy should be rechecked on a strictly separated test set before clinical deployment.
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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 / 5 minor

Summary. The manuscript proposes a hybrid deep learning architecture that combines a pre-trained ResNet101 with a custom CNN, using feature fusion (an 'IntersectFeatures' operation) to classify kidney CT images into normal, stone, cyst, and tumor. The central claim is that this hybrid model achieves 99.73% training accuracy and 100% test accuracy, with a perfect confusion matrix on a 3,734-image test set, outperforming a standalone ResNet101 and a base CNN. The paper reports hyperparameters, per-epoch training metrics, confusion matrices, and PCA visualizations.

Significance. If the reported result were valid, a perfect four-class classification of kidney CT images would be a practically significant outcome, and the dual-branch ResNet101/custom-CNN fusion idea would merit attention. The paper uses a public dataset and provides a fairly detailed architecture table and hyperparameter list. However, the evaluation protocol described in Section 3.1 explicitly uses test data for validation, and Section 3.2 leaves the augmentation/split order ambiguous. These issues are not cosmetic: they directly undermine the independence of the test set and therefore the paper's headline claim. The significance of the paper is currently contingent on an evaluation that is not demonstrably sound.

major comments (4)
  1. [§3.1, §6.2, Abstract] The validation protocol invalidates the test result as an independent measurement. Section 3.1 states 'Validation is performed on 20% of the testing data,' meaning that a subset of the 3,734 test images was used for model selection, early stopping, or hyperparameter choices during training. The reported 100% test accuracy in Table 6 and the Abstract is therefore not a fresh evaluation on never-before-used data. Since the number of epochs (5) and other choices could have been tuned using the validation split drawn from the test set, the central claim of perfect generalization is unsupported.
  2. [§3.2, Table 2] The relationship between the augmented dataset and the train/test split is ambiguous. The test set size is reported as 3,734 images, which equals 30% of the original 12,446 images, not 30% of the augmented 27,035 images listed in Table 2. The paper does not state whether augmentation was applied before or after the 70/30 split. If augmentation preceded the split, the test set should contain roughly 8,110 images; if augmentation followed the split, the per-class counts in Table 2 cannot be reconciled with a 70/30 split of the original counts. This ambiguity leaves open the possibility that augmented copies of training images appear in the test set, which would inflate the reported accuracy.
  3. [§6.2.2, Figure 6] There is an internal contradiction and a tension with the claimed perfect classification. The text says 'Figure 6 shows the confusion matrix of the ResNet101 model,' but Figure 6 is subsequently described as PCA scatter plots. More importantly, the PCA visualization shows overlapping tumor and stone clusters, which is at odds with the hybrid CNN's reported perfect confusion matrix (zero errors on all four classes). The paper should either reconcile these observations or provide a quantitative explanation of how the feature fusion resolves the overlap.
  4. [§5.1, Table 5, Table 6] The training-time numbers are inconsistent. Section 5.1 states the model required 132 seconds per epoch, and Table 4 specifies 5 epochs; this amounts to about 11 minutes of training. Table 6, however, reports a hybrid CNN training time of 1:25:02 (85 minutes). Additionally, Table 5 reports 100% precision, recall, and F1 from Epoch 3 onward while accuracy is below 100% (99.45% and 99.73% in Epochs 4 and 5), which is arithmetically possible only for per-class macro-averaged metrics; the paper should report per-class metrics and state the averaging scheme.
minor comments (5)
  1. [§4, Algorithm 1] The 'IntersectFeatures' operation is never defined mathematically; Algorithm 1 says 'Keep the features extracted from both ResNet101 and custom CNN with higher similarity,' but the similarity criterion, threshold, and feature-alignment mechanism are unspecified. A precise definition is needed for reproducibility.
  2. [§6.2.2] The opening sentence 'Figure 6 shows the confusion matrix of the ResNet101 model' appears to be a leftover from an earlier draft; Figure 6 is captioned as PCA plots. This should be corrected.
  3. [Table 2] The column header 'Number of Kindly Images' contains a typo; it should read 'Number of Kidney Images.'
  4. [§5.3, Table 5] The caption of Table 5 says the table provides a classification report over five epochs, but the reported standard deviation values are not defined in the text; please clarify what quantity the standard deviation is computed over.
  5. [§2, Table 1] The text in Section 2 attributes a precision of 99.88% to Sharma et al. (2024), but Table 1 lists a 96.52% accuracy for that work; the relationship between these numbers should be clarified.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline test accuracy is not an independent measurement: validation is drawn from the test set, so the reported 100% testing score is partly fitted to the target data.

  1. fitted input called prediction [Section 3.1 (Data description and pre-processing) and Section 6.2 (Comparative analysis)]
    "The data is decomposed into training and testing set as: training set contains (70%) and testing contains (30%) of data. Validation is performed on 20% of the testing data. ... To test the performance of the model, a test sample of 3,734 CT images is used. ... hybrid CNN outperforms the others, achieving 100% accuracy, precision, recall, and F1 score."

    The paper declares a held-out testing set, then says validation is performed on 20% of that testing data. Validation is used to monitor or select the model before reporting final test metrics; using a subset of test labels during this process means the reported '100% testing accuracy' is not an independent evaluation on never-seen labels. The 3,734-image test sample either includes the validation images, so the model has been evaluated or selected against those labels, or the validation images were excluded, in which case the reported test sample size and perfect scores need to be re-stated. In either reading, the headline test result is not a clean out-of-sample prediction; it is partly fitted to the data it claims to predict.

full rationale

The paper is an empirical classifier study rather than a mathematical derivation, so most circularity categories do not apply. There are no load-bearing self-citations, no imported uniqueness theorems, and no ansatz smuggled in through prior work. The specific circularity is in the evaluation protocol: Section 3.1 states 'Validation is performed on 20% of the testing data,' and Section 6.2 then reports 100% testing accuracy on the 3,734-image test sample. Because a subset of the test labels is used for validation during model selection or monitoring, the claimed testing accuracy is not an independent measurement; it is partially fitted to the target data. This fits the fitted-input-called-prediction pattern. The augmentation/split-order ambiguity in Section 3.2 further prevents verifying that the 3,734 test images were held out from all augmented training data, though I do not count that ambiguity alone as circularity. Other reported comparisons, such as the base CNN and ResNet101 results and the confusion matrices, are self-contained empirical claims that do not reduce to their inputs by definition.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central performance claim depends on a short list of hand-chosen hyperparameters, an unpublished feature-intersection operation, and an assumed clean split between training and test data. The most consequential hidden assumptions are the use of test data for validation and the unstated augmentation and split order, both of which could explain the perfect reported accuracy.

free parameters (5)
  • Number of training epochs = 5
    Chosen by hand; validation, taken from the test subset, reaches 100 percent at epoch 3, so the 5-epoch cutoff may have been selected based on test-derived validation performance.
  • Learning rate = 0.001
    Chosen by hand; no learning rate sweep is reported.
  • Dropout rate = 0.5
    Chosen by hand; no ablation study is reported.
  • Per-class augmentation counts = Normal +64, Stone +2,541, Cyst +7,418, Tumor +4,566
    The augmentation counts in Table 2 are highly imbalanced across classes; the policy for how many augmented copies per class is not described or justified.
  • IntersectFeatures similarity criterion = Not reported
    The core fusion operation is described only verbally; if a threshold or feature-selection rule exists, it is a free parameter that is not specified.
assumptions (4)
  • domain assumption The Kaggle dataset labels are correct and each image belongs to exactly one of the four classes.
    The entire supervised training and evaluation depend on label accuracy; no expert or second-reader validation is reported.
  • domain assumption Data augmentation was applied only to the training split, not to the test split.
    The paper does not state the order of augmentation and splitting; if augmentation preceded the split, the test set contains augmented copies of training images.
  • domain assumption ImageNet-pretrained ResNet101 features transfer usefully to kidney CT images resized to 224x224.
    CT images are very different from natural images; the paper provides no analysis of domain shift or fine-tuning behavior.
  • ad hoc to paper Validation accuracy on a subset of the test set is an acceptable guide for model selection.
    Section 3.1 explicitly derives validation from the testing data, a protocol that makes the test set non-independent.
invented entities (1)
  • IntersectFeatures operation
    purpose: Merges features from ResNet101 and the custom CNN by keeping features with higher similarity before final classification.
    The operation is described only in prose ('feature mapping filter out the common features with higher similarity'); no mathematical definition, implementation detail, or ablation is provided, and no independent evidence shows it improves over simple concatenation.

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Cite this review

Pith. "Pith review of Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors." pith.science (2026). https://pith.science/paper/CKJ3GMXX

@misc{pith2026250204367,
  author       = {Pith},
  title        = {Pith review of: Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CKJ3GMXX}},
  note         = {Machine review of arXiv:2502.04367}
}
read the original abstract

Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have transformed this field by providing automated and precise analysis of complex medical images. This study introduces a hybrid deep learning model that integrates a pre-trained ResNet101 with a custom CNN to classify kidney CT images into four categories: normal, stone, cyst, and tumor. The proposed model leverages feature fusion to enhance classification accuracy, achieving 99.73% training accuracy and 100% testing accuracy. Using a dataset of 12,446 CT images and advanced feature mapping techniques, the hybrid CNN model outperforms standalone ResNet101. This architecture delivers a robust and efficient solution for automated kidney disease diagnosis, providing improved precision, recall, and reduced testing time, making it highly suitable for clinical applications.

Figures

Figures reproduced from arXiv: 2502.04367 by the authors.

Figure 1
Figure 1. An overview of the augmented data after rotation, flipping, and median filter. 4. Architecture of proposed model: Hybrid CNN The proposed architecture of hybrid CNN shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Architecture of the proposed hybrid CNN model. 4.1. Architecture description [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Schematic outline of hybrid CNN testing. 5.2. Computational setup The model operates on a system equipped with a T4 GPU, 12.7 GB of system RAM, 15.0 GB of GPU RAM, and 112.6 GB of disk space, providing a robust computational environment. The model runs efficiently for up to 10 epochs, using these resources optimally. Beyond this threshold, it fully utilizes the available system and GPU resources, highlighting the sy… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: (left) Model accuracy trend. (right) Model loss trend [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparative analysis of confusion matrix. (left) ResNet101 and (right) Hybrid CNN. 6.2.2. Principal component analysis (PCA) [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Comparative analysis of confusion matrix. (left) ResNet101 and (right) Hybrid CNN. 7. Conclusion To conclude, the hybrid CNN model excelled in classifying kidney CT images, achieving 100% accuracy, preci￾sion, recall, and F1 score. By combining ResNet101 features and a…

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Reference graph

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Reviewed August 9, 2026 · model on record in the stance chip above.