Work

An accurate Alzheimer's disease detection using a developed convolutional neural network model

Year: 2022

Type: article

Abstract: Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households. Early, effective, and accurate detection represents an important blueprint for minimizing Alzheimer's progression risk. The modalities of brain imaging can assist in identifying the abnormalities associated with Alzheimer's disease. This research presents a developed deep learning scheme, which is designed and implemented to classify the brain images into multiclass, namely very mild, moderate, mild, and non-demented. The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in the visual geometric group (VGG16) model and the other related learning models. Consequently, this developed model can assist medical personnel by providing a facilitating tool to identify Alzheimer's disease stage and establishing a suitable medical treatment platform.

Source: Bulletin of Electrical Engineering and Informatics

DOI: 10.11591/eei.v11i4.3659

Authors: Muhanad Tahrir Younis, Younus Tahreer Younus, Jamal Naser Hasoon, Ali Hussain Fadhil, Salama A. Mostafa

Institutions: Mustansiriyah University, Imam Ja’afar Al-Sadiq University, University of Diyala, Tun Hussein Onn University of Malaysia

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Universiti Tun Hussein Onn Malaysia, Mustansiriyah University