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

TL;DR

DGG-XNet is a hybrid deep learning framework combining VGG16 and DenseNet121 for accurate, explainable multi-class brain disease classification, demonstrating high accuracy and interpretability in medical imaging diagnostics.

Contribution

This paper introduces DGG-XNet, a novel hybrid model that fuses VGG16 and DenseNet121 for improved feature extraction and classification in brain disease diagnosis.

Findings

Achieved 91.33% test accuracy on combined datasets.

Model outperformed existing methods in precision, recall, and F1-score.

Grad-CAM visualization enhanced model transparency.

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.