Towards A Binary Classification Algorithm for the Prediction of Alzheimer’s Disease

Alzheimer's Disease (AD) is a significant global health challenge with a rising prevalence, affecting millions of individuals worldwide. With projections estimating a staggering increase in affected individuals, there is an urgent need for advanced diagnostic tools and a deeper understanding of AD's underlying mechanisms. While diagnostic methods predominantly rely on imaging techniques like CT and MRI, utilizing postmortem human brain tissues offers a more intricate structural analysis. This study presents a novel approach to developing a binary classification algorithm for AD using a convolutional neural network (CNN) trained on postmortem tissue samples imaged via immunofluorescence microscopy. The focus on cilia, implicated in signaling deficiencies associated with AD, along with neuronal and astrocyte cell types, aims to capture distinct patterns linked to AD pathology. The CNN-based algorithm identifies structural patterns within tissue samples from AD and non-AD patients, facilitating the differentiation between the two groups. Leveraging CNN's capacity to discern and extract intricate image patterns, this diagnostic tool seeks to enhance AD diagnosis and advance the understanding of the disease's underlying mechanisms. By harnessing postmortem samples and the capabilities of CNNs, this study represents a promising step towards a more refined and potentially more accurate diagnostic tool for Alzheimer's Disease, offering prospects for earlier and more precise detection, crucial in combating this progressive neurodegenerative ailment.