Automatic Classification of Alzheimer's Using Brain MRI Data and the ResNet152V2 Architecture

Alzheimer's disease is a neurological condition that typically affects elderly people and causes memory loss and cognitive deterioration. Effective intervention and therapy for Alzheimer's depend on an early and correct diagnosis. In this article, the model presents a ResNet152V2 architecture-based deep learning method for the cataloging of Alzheimer's disease using brain MRI data. The classification model is built on the ResNet152V2 architecture, which is renowned for its depth and potent feature extraction capabilities. The architecture is a good contender for identifying small brain anomalies suggestive of Alzheimer's disease since it can extract complicated patterns and characteristics from medical imaging. A collection of brain MRI images from healthy people and those with Alzheimer's disease is being pre-processed for investigation. To increase the robustness and precision of the model, the dataset has been expanded and normalized. The findings highlight how important deep learning is for supporting early diagnosis and intervention, providing a noninvasive and effective way to identify those who are at risk of developing Alzheimer's disease.