A hybrid approach for multi-view MRI Alzehimer’s detection using convolutional neural networks and bio-inspired algorithms

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder that remains incurable to date. Therefore, the most important step in treatment remains the early detection of the signs indicating its presence. The sooner these signs are discovered, the sooner preventative care can be administered. Convolutional neural networks (CNNs) have demonstrated impressive performance in medical image analysis; however, they often suffer from suboptimal manual tuning of their hyperparameters. Therefore, we opted for a hybrid method combining them with genetic algorithms (GA) and particle swarm optimization (PSO) to automatically optimize architectures and fusion weights for improved AD detection. Using data obtained from ADNI and Kaggle, our approach achieved 87.4% accuracy, surpassing classical CNNs of the same size and depth. These results highlight the potential of evolutionary optimization for developing reliable diagnostic tools.