DIAGNOSIS OF ALZHEIMER'S DISEASE THROUGH CONVOLUTIONAL NEURAL NETWORK ANALYSIS OF SELECTED HIPPOCAMPAL SLICES IN MRI IMAGES

Abstract

Novel imaging and AI approach, DeepCurvMRI, enhances AD diagnosis. Over 50 million individuals globally are impacted by AD, and that number is projected to increase. It is absolutely critical to detect the disease early and accurately. De-stigmatizing Alzheimer's disease diagnosis, DeepCurvMRI employs Curvelet Transform to extract characteristics from MRI scans. The basic study showed that DeepCurvMRI, which employs a CNN architecture specifically built for AD detection, attained an accuracy rate of 98%. This research takes a look into Xception and DenseNet deep learning models, together with Decision Trees and Voting Classifier. Preliminary research suggests a 99%+ accuracy rate. Numerous implications stem from this research. Doctors are able to intervene earlier when diagnostics are more precise, which benefits patients and their families. Improvements in AD diagnosis also help society with optimal resource allocation and reduced healthcare costs.