Revolutionizing Alzheimer’s: A CNN Model for Early Detection and Accurate Prediction

Alzheimer’s disease (AD), the most common form of dementia, is a devastating neurological disease that progressively impairs memory, damages brain cells and impairs ability to perform daily activities. Harnessing the power of artificial intelligence (AI) through magnetic resonance imaging (MRI) brain scans offers a revolutionary approach to detect and predict this debilitating disease. Using AI, we can classify patients based on their likelihood of developing AD, providing critical information about their condition and enabling early intervention. This innovation aims to create highly accurate diagnostic tools to assist radiologists, physicians and healthcare workers save time, reduce costs, and improve patient outcomes. In recent years, deep learning (DL) algorithms have become transformative tools in AD diagnosis due to their exceptional performance with large datasets. This research used an innovative convolutional neural network (CNN) to facilitate the early identification and classification of Alzheimer’s disease (AD) through MRI imaging. The dataset used in this research was obtained from the OASIS database via Kaggle and encompasses a wide range of patient conditions with detailed structural imaging data, essential for distinguishing different stages of progression of AD. Our model achieved an impressive accuracy of 99.74%, outperforming many related studies. Additionally, we compared these results with previous works using traditional machine learning (ML) algorithms as well as DL algorithms on the same dataset. The results highlight that when processing large medical datasets, DL approaches significantly outperform conventional ML techniques, making them a superior choice for advancing medical diagnostics.