EARLY-STAGE DETECTION AND CLASSIFICATION OF ALZHEIMER'S DISEASE USING CUTTING EDGE TECHNOLOGIES

Alzheimer's disease has emerged as a significant global health issue in recent decades. However, low- and middle-income countries have been slow to acknowledge its severity. According to reports, the global prevalence of Alzheimer's disease is expected to exceed 55 million individuals by the end of 2023, with projections suggesting this figure will triple by 2050. Early detection and appropriate treatment plans can mitigate the risks associated with Alzheimer's disease. Yet, only 10 percent of affected individuals in developing nations receive a formal diagnosis from healthcare professionals. In this study, we propose a novel computer-aided diagnostic (CAD) based Alzheimer's disease detection and classification system that provides precise classification of the disease's current stage. The proposed scheme assists medical professionals to diagnose the Alzheimer's disease with maximum accuracy level 99.8%. Furthermore, the proposed approach improves the existing neuroimaging datasets by conducting analysis and preprocessing techniques such as annotation and labeling. To best of our knowledge, our proposed scheme is the the most appropriate and feasible approach that alleviate the timely detection of Alzheimer's disease, enabling patients to receive appropriate medical treatment and slow the progression of the condition.