Effective Alzheimer’s disease detection using enhanced Xception blending with snapshot ensemble

Abstract

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer's into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer's with individualized care. Subsequent efforts will concentrate on enhancing the model's efficacy via its generalization to a variety of datasets.