Detection of Alzheimer’s Using Convolutional Neural Network

Abstract

Alzheimer’s disease is a neurodegenerative disease that usually starts slow and progressively worsens. Early detection of Alzheimer’s can help us prevent deterioration of the tissue damage in the brain. In this paper, a detailed analysis on detection of Alzheimer’s disease using various Convolutional Neural Network (CNN) architectures in Machine Learning (ML) is shown. This disease causes people to suffer from memory loss and unusual brain tissue damages. Several ML models have been used by researchers for detection of Alzheimer’s. Analyzing Magnetic Resonance Imaging (MRI) scans is a very useful and common practice for the diagnosis of Alzheimer’s disease. ML is used to help the computers to train on the large and complicated datasets and detect the presence of Alzheimer’s disease in brain MRI. In today’s world Deep Learning techniques have successfully brought change in numerous fields including medical image analysis. We propose a deep CNN for Alzheimer’s disease using MRI scan data sets. Our model identifies Alzheimer’s disease in its different stages using different CNN architectures to obtain efficiency and accuracy for early-stage diagnosis of Alzheimer’s disease.