ABSTRACT
To identify useful AD biomarkers using T1 weighted structural magnetic resonance imaging (sMRI) and categorize brain images into AD, mildly demented, moderately demented, non-demented, and very mildly demented categories, this work aims to build a deep learning algorithm. In this study, we modified and trained convolutional neural networks (CNNs) using brain sMRI images from ADNI datasets that are freely accessible online. We have obtained by comparing models such as ResNet and EfficientNet. Image Data Augmentation is the preprocessing method used to the data. Our study will use brain MRI data analysis to diagnose Alzheimer's disease using a deep convolutional neural network. We can distinguish between various phases of Alzheimer's as a result.
