Alzheimer Detection Using Convolutional Neural Network

Abstract

Alzheimer's disease is a severe neurological disorder, ranking as the sixth leading cause of death. Early detection is crucial for timely treatment and potentially preventing further brain tissue damage. This paper proposes an Alzheimer's detection method based on the analysis of MRI images. The existing techniques, such as manual analysis, may lead to misdiagnosis and are time-consuming. Therefore, deep learning, specifically Convolutional Neural Networks (CNNs), is deployed to achieve accurate and efficient results. The proposed approach involves training a CNN architecture using pre-trained models to leverage the power of deep learning. The CNN is designed to analyze MRI images and classify them into three stages: Mild Alzheimer's, Moderate Alzheimer's, and Non-Alzheimer's. By automating the detection process, this method significantly reduces the time required for diagnosis and minimizes the risk of misinterpretation. The research focuses on leveraging the capabilities of deep learning to provide a reliable and early diagnosis of Alzheimer's disease, thereby enabling healthcare professionals to administer timely and appropriate treatments. The trained CNN model demonstrates promising results in accurately identifying different stages of Alzheimer's disease, contributing to the advancement of medical technology in the fight against this devastating condition.