Classification of Alzheimer’s Disease from Cranial MRI Images Using Transfer Learning

Alzheimer's disease (AD) is a progressive neurodegenerative disease mostly seen in the elderly affecting memory, thinking, and the ability to perform daily activities. As the incidence of AD is expected to grow in the coming years, early detection is crucial to handle the disease. At present, the primary imaging modality to diagnose an AD is based on cranial magnetic resonance imaging (MRI). The study aims to investigate the diagnostic capacity of convolutional neural network models via transfer learning in the recognition of brain atrophy and neuronal loss depicting AD from cranial MRI scans. Different neural configurations were tested to determine the top-performing model. The top performing model was obtained by ResNet50 with 0.001 learning rate yielding the highest AUC at 93.5, 85% accuracy, 88% recall, 82% specificity, 80% precision and 84% F1-score. The best model was also incorporated to a simple application developed to predict AD. The early and immediate recognition of AD with transfer learning methods can cascade prompt referral to a neurologist or a geriatric medicine physician as well as the institution of appropriate therapeutic measures to mitigate the adverse impact of brain atrophy and to improve the quality of life of AD patients.