Multimodal Neuroimaging Data in Early Detection of Alzheimer's Disease: Exploring the Role of Ensemble Models and GAN Algorithm

This research aimed to evaluate numerous deep-learning models for Alzheimer's disease detection using several different neuroimaging techniques. Ten recent studies were selected for comparison based on their methodology, conclusions, and limitations. The Generative Adversarial Network (GAN) algorithm is applied fictitiously to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and results are provided. A comparison was made between the results of the GAN algorithm and the selected studies. Evaluation metrics were presented in a table and a graph. The study concludes that ensemble models and multi-modal approaches improve Alzheimer's Disease detection performance. However, there is a need for further work to be done to address issues, including limited sample sizes and a lack of external validation.