Multimodal Classification Of Alzheimer’s Disease And Mild Cognitive Impairment Using Custom Mkscddl Kernel Over Cnn With Transparent Decision-making For Explainable Diagnosis

This article discusses an innovative diagnostic framework that combines Convolutional Neural Networks (CNNs) with Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL) to accurately classify individuals with Alzheimer’s Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses. The approach also identifies nuanced phases within the MCI spectrum and uses scandent decision trees to handle the complexity of neuroimaging data. The model has shown a high level of accuracy in diagnosing cognitive diseases.