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

2025-06-17

AI Summary

To address the low diagnostic accuracy and poor interpretability of automated MRI-based classification for multiple brain disorders—including Alzheimer’s disease and brain tumors—this paper proposes a hybrid deep learning architecture featuring dual backbones: VGG16 and DenseNet121. The model employs feature-level fusion to jointly leverage hierarchical spatial representations and dense feature reuse, while integrating Grad-CAM for transparent, class-specific decision visualization. To our knowledge, this is the first framework achieving a closed-loop balance between high accuracy and strong interpretability in multi-class brain disease classification. Leveraging transfer learning and joint training on multi-center MRI data, the model achieves 91.33% accuracy on a fused BraTS 2021 and Kaggle dataset, with precision, recall, and F1-score all exceeding 91%, significantly outperforming single-backbone baselines.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Accurate diagnosis of brain disorders using MRI
• Inefficient and error-prone manual MRI analysis methods
• Need for interpretable deep learning in medical imaging

Innovation

• Hybrid VGG16 and DenseNet121 for feature extraction
• Grad-CAM visualization for model transparency
• Multi-class brain disease classification with 91.33% accuracy