Edge-Aware Dual-Branch CNN Architecture for Alzheimer's Disease Diagnosis.
Authors
Affiliations (6)
Affiliations (6)
- Institute of Visual Informatics, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, 43600, Malaysia.
- Institute of Visual Informatics, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, 43600, Malaysia. [email protected].
- Department of Petroleum Project Management, College of Industrial Management of Oil and Gas, Basrah University for Oil and Gas, Basra, Iraq. [email protected].
- College of Mathematics and Computer, Xinyu University, Xinyu, 338004, China.
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia, Semenyih, 43500, Malaysia.
- Department of Information Engineering, Southwest Jiaotong University Hope College, Jintang County, Chengdu, 610400, China.
Abstract
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.