Improving Alzheimer’s Disease Prediction Through Convolutional Neural Networks

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is the sixth leading cause of death in the United States. It is characterized by the gradual decline of cognitive function, including memory, language, and reasoning. $A D$ is caused by the accumulation of amyloid plaques and tau tangles in the brain, which damage nerve cells and impair their function. In this project, we developed a deep learning (DL) model to classify MRI images of AD patients from healthy controls. We used a convolutional neural network (CNN) architecture, which is a type of deep learning model that is well-suited for image classification tasks. We trained our model on a dataset of MRI images. We evaluated the performance of our model on a held-out test set of MRI images. Our model achieved an accuracy of $98 \%$, which is significantly higher than the accuracy of other methods that have been used to classify AD from MRI images. Our results suggest that deep learning models can be used to accurately classify AD from MRI images. This could lead to the development of new diagnostic tools for AD and could help to improve the early detection and treatment of this disease.