DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI
This work addresses the challenge of inefficient and error-prone manual MRI analysis for medical professionals, though it is incremental as it combines existing models.
The paper tackled the problem of accurate diagnosis of brain disorders like Alzheimer's disease and brain tumors from MRI images by proposing DGG-XNet, a hybrid deep learning model, which achieved a test accuracy of 91.33% with precision, recall, and F1-score all over 91%.
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.