Multi-class Alzheimer’s disease classification using VGG19 transfer learning with hybrid Harris Hawks Optimization and random forest

Accurate cognition-state classification of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) is essential for early evaluation of the disease. Existing deep-learning approaches often retain redundant features, depend on dataset-specific configurations, and provide limited methodological optimization. This study proposes a transfer-learning-based MRI classification framework integrating spatial and intensity normalization, histogram equalization, double bilateral filtering, VGG19-based deep feature extraction, Harris Hawks Optimization with Simulated…

Ethics approval and consent to participate This study used de-identified MRI data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data were accessed through the official ADNI Data Repository following the required d... [1179 chars]