Alzheimer’s disease multiclass detection through deep learning models and post-processing heuristics

Alzheimer’s disease (AD) significantly impacts millions globally, causing progressive memory loss and cognitive decline. While a cure remains elusive, early detection can mitigate effects and improve quality of life. Recent AD research has shown promise using deep learning algorithms on brain MRI images for stage prediction. This paper introduces a novel approach integrating two CNN algorithms, ResNet and EfficientNet, with a post-processing algorithm to enhance AD diagnosis. Empirical analyses on public datasets ADNI and OASIS evaluate the technique, leveraging the complementary strengths of the CNN models and a weighted averaging ensemble learning method. The proposed approach’s uniqueness lies in combining multiple CNN architectures with a specialised post-processing algorithm. Notable accuracies achieved are 98.59% for EfficientNet, 94.59% for ResNet, and 98.97% with post-processing on ADNI, and 97.25% for EfficientNet, 99.36% for ResNet, and 99.41% with post-processing on OASIS. This work addresses existing methods’ limitations and demonstrates superior predictive performance, contributing to AD diagnosis advancements and highlighting deep learning’s potential in healthcare applications.