Improved Neural Network-Based System for Early and Accurate Diagnosis of Alzheimer Disease

Abstract

Alzheimer's disorder is a neurological condition that develops over time and mainly impacts cognitive processes like memory, thought, and behavior. It is one of the most typical reasons for dementia, a syndrome marked by a loss of cognitive ability that interferes with individual daily activities. Recent techniques for diagnosing Alzheimer's illness frequently combine positron emission tomography (PET) scans with magnetic resonance imaging (MRI), which can identify mutations in the brain caused by the illness, such as the buildup of beta-amyloid plaques and tau tangles. Furthermore, analysis of blood samples and cerebrospinal fluid is also a widely used method for the diagnosis of Alzheimer’s disease. Machine learning and deep learning-based techniques play a vital role in examining complex structures in brain images and other data, contributing to the timely and precise identification of Alzheimer's disease. Artificial intelligence-based techniques can help prompt detection and treatment, leading to more efficient care for Alzheimer's disease. This study uses convolutional neural networks (CNN) with MRI-based datasets for early and accurate diagnosis of Alzheimer’s disease. The proposed approach has shown excellent results in AD diagnosis.