Exploring Deep Learning Models for Early-Stage Alzheimer’s Disease Prediction: A Comparative Study

A progressive neurological disease, alzheimer's disease critically reduces memory and cognitive ability early detection is therefore very important in slowing down its development. This study utilizes MRI data from the Open Access Series of Imaging Studies (OASIS) dataset to evaluate deep learning models for the early prediction of Alzheimer's Disease. This study evaluates and compares many prominent deep learning-based architectures including ResNet-50, VGG-16, MobileNet, and InceptionResNetv2. Normalizing, resizing, and augmenting data helped to improve model resilience and solve data imbalance. Key performance metrics such as accuracy, precision, recall, F1 score, sensitivity, and specificity are helped to evaluate the efficiency of models. Although all models show good ability in spotting early AD symptoms, the findings of this work show feasibility of deep learning models in neuroimaging-based early AD prediction and offer understanding of model selection depending on clinical or computational requirements. Future research might concentrate on explainable artificial intelligence techniques to improve clinical applicability and diagnostic trustworthiness and multimodal data integration.