Improving Alzheimer’s Diagnosis with Deep Learning and Pre-Trained CNN Models

Over the past decade, machine learning techniques, including predictive modeling and pattern recognition, have played a growing role in biomedical sciences, ranging from drug delivery systems to medical imaging. These advancements have enabled researchers to better understand complex problems and address challenging medical issues. Convolutional neural network(CNN) variations were used to identify between Alzheimer’s disease person and healthy person. The significance of classifying Alzheimer medical data is in the possibility of building a predictor model or system to identify the illness type from healthy individuals or to determine the disease’s stage. Selecting the most discriminative features has always been the most challenging part of classifying clinical data, such as Alzheimer’s disease. With Convolutional Neural Networks (CNN) and the well-known architectures Alexnet, VGG, and Nasnet were able to effectively differentiate functional MRI data from persons with Alzheimer’s disease from that of healthy controls.