Applying Convolutional Neural Network (Cnn) In Diagnosing Alzheimer’s Disease

Abstract

This thesis addresses the imperative need for bridging the gap between theoretical machine learning frameworks and their practical clinical applications in the diagnosis of Alzheimer's Disease (AD) through the deployment of a trained Convolutional Neural Network (CNN) model onto a web platform. The demand for accurate and efficient diagnostic tools for AD is on the rise, necessitating innovative solutions that can seamlessly integrate machine learning models into clinical workflows. The research identifies the challenge of deploying machine learning models in real-world settings and proposes a solution by leveraging web-based platforms for AD diagnosis. The study meticulously outlines the methodologies employed in training, validating, and deploying the CNN model, shedding light on the intricacies of model interaction with the interface and backend architecture. Through a comparative analysis, the performance of two prominent models, EfficientNetB7 and Vision Transformer (ViT), is evaluated. The results reveal that EfficientNetB7 achieves remarkable accuracy, with a validation rate of 93.5%, positioning it as a robust and deployable solution for early AD detection in clinical settings. However, ViT, while exhibiting competitive accuracy of 87.4%, encounters deployment challenges attributed to incompatibilities in the training environment. These findings underscore the practical considerations and challenges associated with deploying machine learning models in real-world healthcare settings. Overall, the thesis validates the potential of user-friendly web applications in facilitating the deployment of sophisticated machine learning models for medical diagnostics, setting a precedent for future innovations in AD diagnosis and beyond.