A Novel CNN Architecture for Alzheimer’s Disease Classification Using MRI Data

Abstract

Accurate categorization of Alzheimer’s disease is crucial for medical diagnosis
and the development of therapeutic strategies. Deep learning models
have demonstrated significant potential in this endeavor, however, they frequently
encounter difficulties because of the intricate and varied characteristics
of Alzheimer’s disease. To tackle this difficulty, we suggest a new and innovative
architecture for Alzheimer’s disease classification using MRI data. This
design is named Res-BRNet, and it combines deep residual and boundary-based
convolutional neural networks (CNNs). Res-BRNet utilizes a methodical fusion
of boundary-focused procedures within adapted spatial and residual blocks. The
spatial blocks retrieve information relating to uniformity, diversity, and boundaries
of Alzheimer’s disease, although the residual blocks successfully capture
texture differences at both local and global levels. We conducted a performance
assessment of Res-BRNe. The Res-BRNet surpassed conventional CNN models,
with outstanding levels of accuracy (99.22%). The findings indicate that
Res-BRNet has promise as a tool for classifying Alzheimer’s disease, with the
ability to enhance the precision and effectiveness of clinical diagnosis and treatment
planning.