Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network

Abdullah Al Noman; K. M. Tahsin Kabir; Mahin Montasir Afif; Md. Ashraful Babu; Md. Mortuza Ahmmed; Md. Mostafizur Rahman; Mufti Mahmud

REVIEW 1 major objections 5 minor 18 references

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network

T0 review · 1 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Adding 10% synthetic MRI images yields the best tumor-classification accuracy in this study's experiments.

desk verdict A useful GAN-ratio sweep undermined by inconsistent data-split arithmetic; the trend is plausible, but the headline numbers are not currently reproducible. read the letter →

arxiv 2506.17165 v1 pith:G6FAZGHY submitted 2025-06-20 eess.IV cs.AIcs.CV

Mahin Montasir Afif , Abdullah Al Noman , K. M. Tahsin Kabir , Md. Mortuza Ahmmed , Md. Mostafizur Rahman , Mufti Mahmud , Md. Ashraful Babu This is my paper · ORCID

classification eess.IVcs.AIcs.CV

keywords braintumorclassificationGANaugmentationDCGANsyntheticmedicalimagesconvolutionalneuralnetworkdataratioMRI

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 seeks to determine how much GAN-generated data a CNN can tolerate when learning to classify brain tumor MRI scans. It reports that the best result comes from a small 10% synthetic share, yielding 95.2% accuracy on real test images, slightly above the 94.7% accuracy of a fully real training set. Beyond that point, accuracy falls steadily to 65.4% when all training images are synthetic. The point matters because medical datasets are often too small, and knowing the safe ceiling for synthetic augmentation guides how far GANs can stretch limited data.

What carries the argument

The machinery is a DCGAN (a deep convolutional generative adversarial network) trained over 1,000 epochs to synthesize 1,000 brain MRI images, which are then blended with real images into 11 training sets covering real-to-GAN ratios from 100:0 to 0:100. Each training set contains exactly 1,000 images, and a fixed custom CNN with three convolutional layers, ReLU activations, max-pooling, dropout, and a sigmoid output is trained on each and evaluated on a real test set. The ratio grid is the load-bearing device: it makes the performance decline visible as a function of synthetic share, and the evaluation metrics (accuracy, precision, recall, F1-score, AUC) tie that decline to the model's sensitivity to domain shift.

What would settle it

Compare the actual file names in the BR35H 'yes' and 'no' folders with the images assigned to GAN training, CNN training, and testing. If any real test image was also used to train or validate the GAN or the CNN, the reported accuracy numbers cannot be interpreted as generalization to unseen real scans.

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Extended reading notes

Core claim

The paper's central claim is that CNN performance on real brain tumor MRI is sensitive to the proportion of GAN-generated images mixed into the training set. With 900 real and 100 synthetic images (10% GAN), the custom CNN reaches 95.20% accuracy, 94.31% precision, 96.20% recall, 95.25% F1-score, and an AUC of 0.98, slightly better than the 94.70% accuracy of fully real training. Every further increase in synthetic share lowers accuracy, down to 65.40% at 100% GAN, with AUC falling to 0.71. The authors interpret this as small-scale GAN augmentation acting as a regularizer, while heavy reliance on synthetic data introduces a domain shift that hurts generalization to real scans.

Load-bearing premise

The load-bearing premise is that the real images used for GAN training, CNN training, and testing are disjoint; the paper's stated counts (1,000 for CNN training, 3,000 for GAN training, and 500 or 1,000 test images) exceed the 3,000 images in the dataset, so overlap is possible and would make the test set not independent.

Editorial extensions

If this is right

A small dose of GAN-generated images, around 10% of the training set, can slightly improve accuracy over all-real training, suggesting a mild regularizing effect.
Increasing the synthetic share beyond roughly 50% leads to a pronounced drop in accuracy, recall, and F1-score, so synthetic data should not dominate training in this setting.
Even at 100% synthetic training, the model retains some discriminative ability (65.4% accuracy, AUC 0.71), meaning GAN images carry real signal but not enough to replace real scans.
The monotone decline across all metrics gives practitioners a concrete warning that domain shift from synthetic to real data grows with synthetic proportion.
Future generators, such as diffusion models or domain-adapted GANs, would need to close the remaining realism gap before higher synthetic ratios become safe.

Reading between the lines

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

Editorial inference: because the reported partition sizes (1,000 for CNN training, 3,000 for GAN training, and 500 or 1,000 for testing) do not sum to the 3,000 available images, the performance differences could partly reflect data leakage rather than the synthetic ratio itself.
Editorial inference: the 0.5 percentage-point gap between the 10%-GAN and 0%-GAN conditions is small, so the claimed regularizing benefit may not survive repeated runs with different random seeds.
Editorial inference: a direct testable extension would repeat the ratio grid on a fully disjoint split, with held-out real images never shown to the GAN or the CNN, to confirm whether the monotone decline in accuracy is robust.
Editorial inference: the optimal synthetic share likely depends on GAN fidelity and dataset size; with higher-quality generators, the performance peak could shift toward larger synthetic proportions.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Read the paper. My take: the 11-point DCGAN ratio sweep on BR35H is a practical, useful addition to the GAN-augmentation literature, but the data-split inconsistencies are serious enough that the quantitative claims should not be trusted until fixed.

What is actually new: most prior work compares one augmentation fraction against none, or reports a single synthetic/real mix. This paper traces the full curve from 0% to 100% GAN on a fixed CNN architecture, with accuracy, precision, recall, F1, and AUC at each point. The fully synthetic baseline (65.4% accuracy) and the monotone decline beyond 10% give a concrete mapping of the trade-off for this dataset. That is worth having, even if the qualitative direction is not surprising.

Where it falls down: the split arithmetic does not hold. Section 2.1 says the 3,000 images were partitioned into 1,000 for CNN training, 3,000 for GAN training, and 500 for testing. Those numbers sum to 4,500. Section 2.4 then says the test set contains 1,000 real images, and Section 3 says each configuration used 800 training / 200 validation. You cannot fit all of that into 3,000 images with disjoint sets. If the GAN really trained on all 3,000, the test set overlaps GAN training. If not, the actual partition is unspecified. Either way, Table 2 is unverifiable, and the claimed 10% optimum could be an artifact of leakage. This is not a cosmetic complaint; it directly affects every row of the central comparison.

Other soft spots: single-run experiments with no error bars, no quantitative evaluation of GAN output quality (only visual inspection), and no released code or data. The regularization explanation in Section 3.3 is attached post hoc to the observed argmax, so it is interpretation rather than a tested hypothesis. The citation pattern looks reasonable; the self-citations are to related recent work and not load-bearing.

For all that, the paper shows sensible experimental intent and the broad conclusion (small synthetic fractions help, large ones hurt) is consistent with the literature. It deserves a serious referee who will require a clean re-run with explicitly disjoint splits, multiple seeds, and quantitative GAN fidelity metrics. As it stands, I would not cite it, and I would not rely on the numbers. But it is a good reading-group case study on why split documentation matters, and with the arithmetic fixed it could be a solid empirical contribution.

Recommendation: send to peer review only after the authors resolve the split contradictions; desk rejection would be premature given the usefulness of the sweep.

Referee Report

1 major / 5 minor

Summary. The paper trains a DCGAN on the BR35H brain MRI dataset, generates 1,000 synthetic images, and constructs 11 training sets with real-to-GAN ratios from 100:0 to 0:100. A custom CNN with three convolutional layers is trained on each mixture and evaluated on a real test set. The reported best performance is at 10% GAN (95.20% accuracy), with performance generally declining as the synthetic fraction increases, down to 65.40% at 100% GAN. The authors conclude that small amounts of GAN data can regularize training, while heavy reliance on synthetic data hurts generalization.

Significance. If the central trend holds, the paper addresses a practically important question for medical imaging: how much synthetic data should be mixed with real data when training classifiers. The systematic sweep over 11 ratios, the presentation of full metric tables, and the reproducible architecture description are strengths. However, the core quantitative claims currently rest on an internally inconsistent data-split description and on single-run results without uncertainty estimates. The finding that 10% GAN is better than 0% GAN is a 0.5 percentage-point difference that may be within run-to-run noise, so the paper's main recommendation is not yet established at the level of rigor expected for a journal publication.

major comments (1)

[§2.2, §3.1] The quality of the synthetic images is not quantitatively assessed. The paper relies only on visual inspection and loss curves, but no Frechet Inception Distance, Inception Score, or similar measure is reported. Given that the central argument is about how synthetic data quality affects downstream CNN performance, a quantitative quality measure would substantially strengthen the interpretation of the ratio-dependent results. If such measures are unavailable, the authors should at least acknowledge this limitation explicitly in the Discussion.

minor comments (5)

[§2.1] The sentence 'The dataset was partitioned into 1,000 images for further training, 3,000 for GAN training, and 500 reserved as an independent test set' is also internally unclear because 'further training' is not defined; please rewrite to give exact counts for each role and clarify that all subsets are disjoint.
[Abstract and §2.2] The notation alternates between 'DCGAN' and 'DC-GAN'; please use one consistent abbreviation throughout.
[Table 2] The column header 'Data Distribution' would be clearer as 'Real:GAN Ratio' or 'Percentage of GAN Data'.
[Algorithm 1] Line 11 says 'Optionally evaluate on validation set and store metrics,' but the paper does not describe how validation is used (e.g., for early stopping or model selection). Please specify whether validation influenced the final model or was only used for reporting.
[References] Several references are missing spaces or have typographical issues (e.g., 'Synthetic Data· Brain Tumor Classification' in the keywords line and the reference formatting in Ref. [8]). Please run a final copyediting pass.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central results are direct empirical measurements on a held-out test set, not predictions derived from fitted inputs or self-citations.

full rationale

The paper reports a controlled experiment: it trains a CNN on 11 real/GAN image ratios and evaluates each on the same real test set. The main claims—that accuracy declines with increasing GAN proportion and that the 10% GAN ratio gives the best observed accuracy—are read directly from Table 2's measured metrics. There is no fitted parameter that is later renamed as a prediction, no equation that defines the output in terms of the input, and no uniqueness theorem or load-bearing result imported from prior work. The Section 3.3 explanation that a small GAN fraction may act as a regularizer is post-hoc interpretation, not a derivation, so it does not create circularity. The paper cites two works involving overlapping authors (references [2] and [6]) but only as methodological references for evaluation metrics and related deep learning approaches; no central argument depends on them. The internal inconsistency in the reported data split (1,000 + 3,000 + 500 = 4,500 from 3,000 images, and a later statement of a 1,000-image test set) is a serious correctness and reproducibility concern, but it is an empirical validity issue, not circular reasoning. Accordingly, the circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper's central claim rests on dataset labels, on the assumed correctness of GAN class assignment without quantitative validation, and on disjoint partitions that the text itself contradicts. No new theoretical entities are introduced; the only data-selected value is the optimal ratio, which is the argmax of the reported accuracy curve.

free parameters (1)

Optimal real-to-GAN ratio = 10% GAN (900 real, 100 GAN)
Selected post hoc as the best-performing point in the 11-ratio sweep (Table 2); used to support the claim that small synthetic proportions improve generalization, so the claim is a restatement of the data maximum.

assumptions (4)

domain assumption BR35H dataset labels (tumor vs. non-tumor) are correct.
The paper relies on the public BR35H labels as ground truth for both training and testing without independent radiological verification.
domain assumption GAN-generated images are assigned the class they were generated for.
The 1,000 synthetic images are labeled 500 tumor and 500 non-tumor based on generator class, with no expert evaluation or quantitative fidelity check (e.g., FID or SSIM).
domain assumption The reported train, validation, GAN-training, and test partitions are disjoint.
The paper states partitions summing to 4,500 images while the available dataset is 3,000 images; test-set independence from GAN training is not established.
domain assumption The CNN and DCGAN hyperparameters are sufficient for the observed performance.
Several hyperparameters (CNN epochs, dropout rate, GAN architecture details) are not reported, so reproducibility relies on unspecified choices.

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Cite this review

Pith. "Pith review of Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network." pith.science (2026). https://pith.science/paper/G6FAZGHY

@misc{pith2026250617165,
  author       = {Pith},
  title        = {Pith review of: Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G6FAZGHY}},
  note         = {Machine review of arXiv:2506.17165}
}

read the original abstract

Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains high sensitivity and precision in tumor classification, even when trained predominantly on synthetic data. When only a small portion of GAN data was added, such as 900 real images and 100 GAN images, the model achieved excellent performance, with test accuracy reaching 95.2%, and precision, recall, and F1-score all exceeding 95%. However, as the proportion of GAN images increased further, performance gradually declined. This study suggests that while GANs are useful for augmenting limited datasets especially when real data is scarce, too much synthetic data can introduce artifacts that affect the model's ability to generalize to real world cases.

Figures

Figures reproduced from arXiv: 2506.17165 by the authors.

**Figure 1.** Types of tumor in human brain [12] 1.1 Convolutional Neural Networks for Brain Tumor Classification Convolutional Neural Networks (CNNs) [11] are a type of deep learning model designed to process image data by automatically extracting relevant visual features from basic shapes and edges to complex structures like tumor patterns. A study by [5] achieved 94.58% accuracy using transfer learning with AlexNet/VGG-16 on … view at source ↗

**Figure 2.** Methodology Overview: A comprehensive outline of the systematic pro [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** A tailored convolutional neural network designed and optimized specifi [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Tumor and Non-Tumor GAN Training Curves Across 1000 Epochs [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Progressive visual results produced by the GAN at various training stages [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Training and validation accuracy and loss curves on the best-performing dataset proportion [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

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

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Diagnostics14(16), 1714 (2024)

Aamir, M., Namoun, A., Munir, S., Aljohani, N., Alanazi, M.H., Alsahafi, Y., Alotibi, F.: Brain tumor detection and classification using an optimized convolu- tional neural network. Diagnostics14(16), 1714 (2024)

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Ahmmed, M.M., Noman, A.A., Afif, M.M., Kabir, K.M.T., Rahman, M.M., Mah- mud, M.: A model-mediated stacked ensemble approach for depression prediction among professionals (2025),https://arxiv.org/abs/2506.14459 12 M. M. Afif et al

work page Pith review arXiv 2025

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Scientific Data 11, 259 (2024)

Akbar, M.U., Larsson, M., Blystad, I., Eklund, A.: Brain tumor segmentation using synthetic mr images - a comparison of gans and diffusion models. Scientific Data 11, 259 (2024)

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Springer (2006)

Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)

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Computers in Biology and Medicine111, 103345 (2019)

Deepak, S., Ameer, P.: Brain tumor classification using deep cnn features via trans- fer learning. Computers in Biology and Medicine111, 103345 (2019)

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Eity, S.N., Afif, M.M., Fairooz, T., Ahmmed, M.M., Miah, M.S.U.: Dgg-xnet: A hybrid deep learning framework for multi-class brain disease classification with explainable ai (2025),https://arxiv.org/abs/2506.14367

work page Pith review arXiv 2025

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Advances in neural infor- mation processing systems27 (2014)

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Advances in neural infor- mation processing systems27 (2014)

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