Advanced Multimodal Deep Learning for Predicting Cognitive Decline in Alzheimer’s Disease

Alzheimer's disease, a neurodegenerative condition that is progressive, is believed to be one of the big threats to global healthcare. Hence, there is a very severe need for early and accurate diagnosis. An advanced multimodal deep learning framework, which is based on DenseNet model and known for being dense in terms of its connectivity and reusing the same features, will improve the propagation of features and mitigate the problem of vanishing gradients. MRI scans from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset have been used in this study for the experiment to predict cognitive decline and classify stages of it. The performance of DenseNet was also compared and evaluated with other models, among which there are several widely accepted models: CNN, VGG-16, and MobileNet. The results showed DenseNet had the highest test accuracy, which happened at 81.5% while the rest happened at less than 60% in detecting subtle neurodegeneration patterns associated with Alzheimer's progress, making it one of the most promising tools for the early stage of disease detection. This comparison may highlight the potential change that may be brought about by DenseNet in Alzheimer's disease research and later impact improved clinical results owing to timely intervention.