Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture
Yunendah Nur Fuadah, Inung Wijayanto, NOR KUMALASARI CAESAR PRATIWI, Fauzi Frahma Taliningsih, Syamsul Rizal, Muhammad Adnan Pramudito
Abstract
Abstract Alzheimer’s disease is a type of brain disease that indicate with memory impairment as the early symptoms. These symptoms occur because the nerve in the brain involved in learning, thinking and memory as cognitive function have been damaged. Alzheimer is one of diseases as the leading cause of death and cannot be cured, but the proper medical treatment can delay the severity of the disease. This study proposes the Convolutional Neural Network (CNN) using AlexNet architecture as a method to develop automated classification system of Alzheimer’s disease. The experiment is conducted using Magnetic Resonance Imaging (MRI) datasets to classify Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented from 664 MRI datasets. From the experiment, this study achieved 95% of accuracy. The automated Alzheimer’s disease classification can be helpful as assisting tool for medical personnel to diagnose the stage of Alzheimer’s disease so that the appropriate medical treatment can be provided.