A Modern Generative AI Framework for Alzheimer Detection Leveraging Autoencoders and Softmax Classifier

This study proposes a modern generative AI framework for Alzheimer's detection that combines autoencoders with a Softmax classifier. Autoencoders are utilized to extract meaningful and compact features from brain imaging data, enabling efficient dimensionality reduction and noise filtering. The Softmax classifier, built atop the encoded features, ensures accurate disease classification by distinguishing Alzheimer's patients from healthy individuals. The framework leverages advanced neural architectures to improve diagnostic accuracy and computational efficiency. Experimental results demonstrate its potential to enhance early detection, aiding in timely intervention and treatment planning. In this study, the model achieved a classification accuracy of 89%, which is a strong result.