Explainable bidirectional encoder representations from image transformers for Alzheimer's disease prediction

People who have Alzheimer's disease (AD) experience a progressive decline in their neurological function, which leads to mental deterioration and diminished memory abilities, and altered behaviors that affect both patients and their care providers severely. Diagnosis of the disease at an early stage and with precision helps ensure appropriate intervention strategies.

Modern artificial intelligence (AI) technology is promising in medical use for imaging and diagnostic work, specifically involving AD detection and classification. This study aims to develop and evaluate an explainable transformer-based framework that leverages Bidirectional-Encoder representations from Image Transformers (BEiT) to automatically classify AD stages from magnetic resonance imaging (MRI) brain scans.

The proposed framework employs BEiT as a feature extractor on a dataset of 8511 MRI brain images categorized into three diagnostic groups (mild, moderate, and no impairment). Class imbalance is addressed through a Wasserstein generative adversarial network with gradient penalty-based oversampling strategy that generates synthetic MRI images for minority classes, and these images are combined with the original scans to form a balanced training set.

The experiments showed outstanding accuracy levels reaching 96%, while the F1-scores indicated 0.94, 1.00, and 0.95 for mild, moderate, and no AD group classifications. Performance evaluation metrics from the study demonstrate strong outcomes with a mean absolute error reaching 0.0727 and Cohen's kappa equaling 0.9451, while Matthews correlation coefficient reached 0.9455 and Hamming loss remained at 0.0365.