Edge-Aware Dual-Branch CNN Architecture for Alzheimer's Disease Diagnosis

The rapid development of machine learning (ML) and deep learning (DL) has greatly advanced Alzheimer's disease (AD) diagnosis. However, existing models struggle to capture weak structural features in the marginal regions of brain MRI images, leading to limited diagnostic accuracy. To address this challenge, we introduce a Dual-Branch Convolutional Neural Network (DBCNN) equipped with a Learnable Edge Detection Module designed to jointly learn global semantic representations and fine-grained edge cues within a unified framework. Experimental results on two public datasets demonstrate that DBCNN significantly improves classification accuracy, surpassing 98%. Notably, on the OASIS dataset, it achieved an average accuracy of 99.71%, demonstrating strong generalization and robustness. This high diagnostic performance indicates that the model can assist clinicians in the early detection of Alzheimer's disease, reduce subjectivity in manual image interpretation, and enhance diagnostic consistency. Overall, the proposed approach provides a promising pathway toward intelligent, interpretable, and computationally efficient solutions for MRI-based diagnosis, offering strong potential to support early clinical decision-making.