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All Journal Bulletin of Electrical Engineering and Informatics
Ali Hussain Fadhil
University of Diyala
Electrical & Electronics Engineering

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An accurate Alzheimer's disease detection using a developed convolutional neural network model Muhanad Tahrir Younis; Younus Tahreer Younus; Jamal Naser Hasoon; Ali Hussain Fadhil; Salama A. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3659

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.
Co-Authors Jamal Naser Hasoon Muhanad Tahrir Younis Salama A. Mostafa Younus Tahreer Younus