DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI

Mahin Montasir Afif; Md. Mortuza Ahmmed; Md Saef Ullah Miah; Sumshun Nahar Eity; Tanisha Fairooz

REVIEW 5 major objections 5 minor 1 cited by

DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A VGG16 and DenseNet121 feature fusion reaches 91.33% test accuracy on a three-class brain MRI task, outperforming both single backbones.

desk verdict Routine feature-fusion paper whose 91% accuracy rests on a slice-level random split that may leak patient information into the test set. read the letter →

arxiv 2506.14367 v1 pith:CY2WV5RH submitted 2025-06-17 cs.CV

Sumshun Nahar Eity , Mahin Montasir Afif , Tanisha Fairooz , Md. Mortuza Ahmmed , Md Saef Ullah Miah This is my paper · ORCID

classification cs.CV

keywords brainMRIclassificationhybriddeeplearningVGG16DenseNet121globalaveragepoolingfusionGrad-CAMexplainabilityAlzheimer'sdiseasetumor

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 proposes DGG-XNet, a hybrid deep learning model that runs every MRI slice through two pre-trained convolutional backbones, VGG16 and DenseNet121, pools the resulting feature maps with global average pooling, concatenates the pooled vectors, and sends them through dense layers to a three-way softmax (Tumour, Normal, Alzheimer's). Trained on a balanced dataset built from the BraTS 2021 tumor volumes and a public Alzheimer's MRI collection, the model reports 91.33% test accuracy, with macro-averaged precision, recall, and F1-score near 0.90, on a 150-image balanced test set. The paper's claim is that this feature-level fusion beats every single backbone it compares against, including VGG16 at 84.67% and DenseNet121 at 82.67%, and that Grad-CAM plus Integrated Gradients show the decisions rest on interpretable brain regions. A sympathetic reader would take this as evidence that fusing complementary CNN feature extractors is a promising, more transparent route for computer-aided screening of neurological disease.

What carries the argument

The load-bearing mechanism is dual-branch feature fusion with global average pooling. Each input image $x$ is passed through VGG16 and DenseNet121; the pooled branch outputs are concatenated into $F = \mathrm{Concat}(\mathrm{GAP}(VGG16(x)), \mathrm{GAP}(DenseNet121(x)))$, and $F$ is transformed by batch-normalized dense layers with ReLU, dropout, and a softmax head. The concat fusion is what allows the final classifier to combine VGG16's spatial hierarchy with DenseNet121's densely reused features before making the three-way decision.

What would settle it

Re-run the exact training protocol but split the data by patient or 3D volume such that every slice from one volume lands in a single partition; if accuracy falls well below 91.33%, or if a nearest-neighbor check shows train/test slices from the same volume, the reported number reflects identity leakage rather than generalizable classification.

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Extended reading notes

Core claim

The central discovery is that fusing VGG16 and DenseNet121 at the feature-vector level improves three-class brain MRI classification beyond either backbone alone: DGG-XNet reaches 91.33% test accuracy while VGG16 alone reaches 84.67% and DenseNet121 alone reaches 82.67% on the same balanced 150-image test set. The authors attribute the gain to VGG16's strong hierarchical spatial features complementing DenseNet121's dense connectivity and feature reuse, together with ImageNet-initialized transfer learning, downsampling to 500 images per class, and early stopping.

Load-bearing premise

The load-bearing premise is that the stratified random split of individual 2D MRI slices into training and test sets keeps the test set independent; since slices are drawn from the same 3D volumes without patient-level grouping, slices from the same patient can appear on both sides, which would let the model memorize patients rather than learn the disease.

Editorial extensions

If this is right

If the reported accuracy holds, feature-fusion hybrids of this kind are a stronger starting point than single CNNs for multi-class neurological screening from 2D MRI slices.
The same training recipe, including balanced downsampling, ImageNet initialization, and early stopping, yields a model whose Grad-CAM and Integrated Gradients maps point to localized brain structures.
The comparison table implies that a family of fusion architectures can be swept by swapping backbones while keeping the global-average-pooling, concatenation, and dense-head design fixed.
The class-wise metrics show the remaining error concentrates on the Alzheimer's class, with precision 0.82 and recall 0.84, while Tumour is classified perfectly at 1.00.
The paper's stated future directions, including 3D volumetric analysis, added modalities, data augmentation, and domain adaptation, are natural next tests of whether the fusion benefit transfers beyond the current 2D slice setting.

Reading between the lines

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

Because the split described in the paper does not group slices by patient or 3D volume, the 91.33% figure could be inflated by same-patient slices appearing in both training and testing; a patient-level evaluation would settle this directly.
The fusion advantage could be probed by ablating the concatenation against attention-weighted fusion or single-branch global average pooling, to see whether the gain is the fusion itself or simply added model capacity.
Grad-CAM and Integrated Gradients maps are shown qualitatively; their faithfulness could be quantified with insertion/deletion or pointing-game metrics.
Because BraTS volumes and the Alzheimer's 2D set differ in acquisition and content, testing DGG-XNet on a third unseen dataset, or with per-scanner cross-validation folds, would show whether the 91.33% transfers across imaging protocols.

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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

Quick take: this is a standard VGG16+DenseNet121 feature-concatenation application, not a new method. The reported numbers are internally consistent, and the paper is clearly written, but the central accuracy claim depends on an unverified data split: BraTS volumes are sliced into 2D axial images, and then the train/test split is done at the slice level without grouping by patient or volume. Slices from the same patient can appear in both training and testing. Because adjacent slices from one volume are highly correlated, the model can exploit within-patient memorization. The stress-test note is correct, and it is the load-bearing issue here.

What the paper does well: it gives a complete pipeline (preprocessing, balanced 500-per-class dataset, stratified split into 1050/300/150), compares against eight baselines, reports macro metrics, and includes Grad-CAM plus Integrated Gradients. The accuracy figures in Table 3 and Table 4 are internally consistent with the confusion matrix. The authors also cite the relevant literature, though the comparison table is a bit thin.

Soft spots, in order of severity. First, the split. Section 3.3 says 'stratified sampling' at the dataset level, but gives no patient or volume identifier. If any BraTS volume contributes slices to both train and test, the test accuracy is inflated, and the comparison against VGG16 and DenseNet121 is also confounded because all baselines use the same leaky split. This is a load-bearing flaw, not a minor one. Second, BraTS slices are labeled Tumour without per-slice verification; some slices near the volume edge may contain little or no tumor, adding label noise. Third, no code or weights are released, no confidence intervals are given, and the test set is only 150 images. Fourth, the novelty is low: concatenating two ImageNet feature extractors with global average pooling is a commonplace transfer-learning pattern.

My verdict: the paper is not nonsense, but it does not currently support the conclusion that DGG-XNet is a reliable CAD tool. If the split is patient-disjoint, the accuracy might be somewhat lower but still plausible; if not, the result is suspect. This is fixable. A serious referee should see it because the flaw is concrete and the fix is straightforward. I would not cite the current version, but I would bring it to a reading group as a cautionary example of slice-level leakage.

Recommendation: major revision with patient-level splitting, per-slice label verification, and code release, or rejection if the authors cannot redo the evaluation.

Referee Report

5 major / 5 minor

Summary. The manuscript proposes DGG-XNet, a hybrid deep learning model that fuses VGG16 and DenseNet121 feature maps via global average pooling, for three-class brain MRI classification (Tumour, Normal, Alzheimer's). The model is trained on T1-weighted slices from BraTS 2021 and a Kaggle Alzheimer's dataset, balanced to 500 samples per class and split 70/20/10 by stratified sampling. The authors report 91.33% test accuracy, macro precision/recall/F1 around 90%, and compare against eight CNN baselines. Grad-CAM and Integrated Gradients are used to illustrate model explanations.

Significance. If the evaluation were reliable, the paper would offer a useful empirical data point on hybrid feature fusion for multi-class brain MRI classification, with an interpretability component that is valuable for clinical CAD. The architecture choice is reasonable, and the comparison to several baselines addresses a practical question. However, the current manuscript does not establish patient-level generalization because the split is slice-based, the tumor labeling is not per-slice verified, and the headline accuracy is inconsistent with the reported per-class metrics. These issues are central to the paper's main claim.

major comments (5)

[Section 3.3 / Table 2] The test split is performed at the slice level with stratified random sampling, but the dataset is constructed by extracting multiple 2D axial slices from each BraTS 2021 volume (Section 3.2). Because no patient or volume identifier is used to group slices, slices from the same patient can appear in both training and test sets. Adjacent T1-weighted slices from one volume are highly correlated, so the model can memorize patient-specific intensity patterns; the reported 91.33% accuracy (Table 3) and the comparison to VGG16/DenseNet121 therefore do not establish generalization to new patients. Please re-split at the patient/volume level, state the number of patients in train/validation/test, and re-run all models in Table 3 under this protocol.
[Section 3.2] BraTS 2021 Task 1 volumes are labeled as a single 'Tumour' class for every extracted 2D slice. In a tumor patient's volume, many axial slices (especially at the top and bottom of the brain) contain no tumor tissue; labeling all slices as Tumour is a patient-level label, not a slice-level diagnosis. Since BraTS 2021 provides segmentation masks, please use them to identify slices that actually contain tumor, or explicitly justify patient-level labeling and show that the selected slices are representative. Without this, the Tumour class definition and the measured accuracy are ambiguous.
[Section 3.1 / 3.2] The Alzheimer's dataset is described as having four categories (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented), but the three-class setup uses only 'Alzheimer's' and 'Normal'. The mapping from the original four classes to these two is never stated, and the class counts after downsampling are given only as 500 per class. Please specify which original labels become 'Alzheimer's', whether 'Non-Demented' serves as 'Normal', and report the per-subclass counts. Otherwise it is unclear whether the Normal and Alzheimer's classes come from the same acquisition protocol and whether 'Normal' in the Alzheimer's dataset is comparable to the healthy tissue seen in BraTS.
[Table 4 / Abstract] The reported metrics are internally inconsistent. On a balanced test set of 50 samples per class, accuracy equals the average of the class recalls, i.e., (1.00 + 0.87 + 0.84)/3 = 90.33%, not 91.33%. The abstract states that precision, recall, and F1-score all exceed 91%, but Table 4 gives a macro F1 of 0.90 and per-class scores of 0.82-1.00. Please correct the numbers and provide the confusion matrix with integer counts so that the accuracy, recalls, and precisions can be cross-checked.
[Section 4 / Table 3] The claim that DGG-XNet outperforms VGG16, DenseNet121, and other baselines is not controlled. Table 3 lists only accuracy values; the manuscript does not state whether each baseline was trained with the same optimizer, learning rate, batch size, early-stopping criterion, data balancing, and train/validation/test split. Without identical training protocols, the accuracy differences cannot be attributed to the proposed fusion architecture. Please describe the common training protocol and report per-model hyperparameter settings.

minor comments (5)

[Section 3.6] The citation '[6]' for Explainable AI refers to the ImageNet paper; please cite the original Grad-CAM and Integrated Gradients references at the point of use.
[Figure 4b] The ROC curves are shown but no AUC values are reported in the text; list the numerical AUC for each class.
[Section 3.2] The preprocessing description says images are 'resized to 224x224x3 and normalized'; state the normalization scheme (e.g., min-max, z-score) and whether normalization parameters were fitted on the training set only.
[Equation (11)] The integrated-gradients formula would benefit from standard notation for the integration path, and reference [27] is for IG2 rather than the standard integrated-gradients method described; please cite the appropriate original reference.
[General] The manuscript does not include a data availability statement or mention whether code will be released; adding these statements would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DGG-XNet's accuracy claim is an empirical measurement on a held-out test split, not a derived quantity that reduces to its inputs.

full rationale

The paper proposes a hybrid CNN and reports test accuracy. The central claim is not derived from an equation that is equivalent to its inputs. Equation (1) defines the fused feature vector as a concatenation of the two backbones' GAP outputs; this is the model architecture, and the reported 91.33% accuracy is measured on the test split in Tables 3 and 4, not fitted to produce that number. The model is trained with standard loss and optimizer equations (3)-(4), and the metrics (5)-(8) are evaluation definitions, not circular predictions. The only self-citation (reference [2], Ayon et al. with author Miah) supports general background about disease burden and is not load-bearing for the architecture or the accuracy claim. Possible slice-level train/test leakage is a data-independence concern and a correctness risk, but it does not make the result circular: the reported accuracy would still be an empirical measurement, just one whose generalization claim is weakened. No uniqueness theorem, ansatz smuggled via citation, or renamed-known-result pattern appears. The performance claim is therefore self-contained as an empirical evaluation, with no circular step identified.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The reported accuracy is a function of the hyperparameters, dataset construction choices, and split procedure listed above. The paper does not derive any of these from first principles, nor does it validate them on an external benchmark, so the central claim should be read as dependent on these choices.

free parameters (6)

Adam learning rate = 0.0001
Chosen by hand in Section 3.4; the final accuracy depends on this optimizer setting.
Early stopping patience = 5 epochs
Set in Section 3.4; determines which epoch's weights are restored and therefore the reported test accuracy.
Input image size = 224x224x3
All images resized to this dimension in Section 3.2 to match pretrained backbones.
Per-class sample count after downsampling = 500
Section 3.2: all classes were downsampled to 500 samples to match the minority class, fixing the total dataset size at 1500.
Train-validation-test split ratio = 70/20/10
Section 3.3: stratified split proportions chosen by the authors.
Fully connected layer configuration = not reported
Section 3.4 describes dense layers and dropout but does not give layer sizes or dropout rate, even though these affect accuracy.

assumptions (6)

standard math Standard deep-learning equations for softmax, cross-entropy, and Adam are correct and applicable.
Invoked in Sections 3.4 and 3.5; no derivation is needed, but the correctness of these standard formulas is assumed.
domain assumption 2D axial slices are representative of the 3D MRI volume for disease classification.
Section 3.2 extracts evenly spaced slices; this requires that slice-level labels match volume-level disease state.
ad hoc to paper BraTS 2021 Task 1 volumes can be labeled as Tumour class for each extracted slice.
Section 3.1 and 3.2 label all extracted BraTS slices as Tumour without per-slice verification of tumor presence.
domain assumption ImageNet-pretrained features transfer to MRI brain images.
Section 3.4 initializes both backbones with ImageNet weights; a common but not guaranteed assumption for medical images.
ad hoc to paper The four Kaggle Alzheimer's classes can be collapsed into Alzheimer's and Normal for this three-class setup.
Section 3.1 mentions the four-class Kaggle dataset but does not describe exactly how those classes map to Alzheimer's and Normal.
domain assumption A stratified random split of slices yields independent train and test sets.
Section 3.3 splits slices without accounting for patient or volume identity, which can cause data leakage.

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Cite this review

Pith. "Pith review of DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI." pith.science (2026). https://pith.science/paper/CY2WV5RH

@misc{pith2026250614367,
  author       = {Pith},
  title        = {Pith review of: DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CY2WV5RH}},
  note         = {Machine review of arXiv:2506.14367}
}

read the original abstract

Accurate diagnosis of brain disorders such as Alzheimer's disease and brain tumors remains a critical challenge in medical imaging. Conventional methods based on manual MRI analysis are often inefficient and error-prone. To address this, we propose DGG-XNet, a hybrid deep learning model integrating VGG16 and DenseNet121 to enhance feature extraction and classification. DenseNet121 promotes feature reuse and efficient gradient flow through dense connectivity, while VGG16 contributes strong hierarchical spatial representations. Their fusion enables robust multiclass classification of neurological conditions. Grad-CAM is applied to visualize salient regions, enhancing model transparency. Trained on a combined dataset from BraTS 2021 and Kaggle, DGG-XNet achieved a test accuracy of 91.33\%, with precision, recall, and F1-score all exceeding 91\%. These results highlight DGG-XNet's potential as an effective and interpretable tool for computer-aided diagnosis (CAD) of neurodegenerative and oncological brain disorders.

Figures

Figures reproduced from arXiv: 2506.14367 by the authors.

**Figure 2.** Architecture of the proposed model [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Training and validation accuracy and loss curves. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Performance evaluation: Confusion Matrix and ROC Curve [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: Explainable AI: Grad-CAM & Integrated Gradients [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
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As the share of GAN-generated brain MRI images in training data rises, CNN tumor classification accuracy falls, with a small 10% synthetic addition giving the best 95.2% accuracy.

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