DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI
Journal:
arXiv
Published Date:
Jun 17, 2025
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.
