Deep Learning Approach to Predict Alzheimer's Disease through Magnetic Resonance Images

Gilang Titah Ramadhan, Yori Pusparani, Isack Farady, Elvin Nur Furqon, Chih Yang Lin, Wen Hung Chao, John Sahaya Rani Alex, Jeetashree Aparajeeta, Chi Wen Lung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Alzheimer's disease is the most common type of dementia that causes many of the functions of the human brain to be severely weakened. To date, there has not been a cure for Alzheimer's disease. Therefore, early diagnosis is needed using MRI images with the help of a classification program. Deep learning using the Convolutional Neural Network (CNN) method is receiving increasing attention because of its excellent performance. Its architecture can be modified according to user needs based on the data to be processed and the approach for classifying, detecting, and segmenting visual objects. In this paper, we offer a classification of Alzheimer's disease using one of the architectures on CNN, namely Visual Geometry Group-19 (VGG-19), with a sagittal view of MRI images with an image size of 229 x 229 pixels. The classification accuracy of the described method is 94% for the validation set.

Original languageEnglish
Title of host publication2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages845-846
Number of pages2
ISBN (Electronic)9798350324174
DOIs
StatePublished - 2023
Event2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
Duration: 17 Jul 202319 Jul 2023

Publication series

Name2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/TerritoryTaiwan
CityPingtung
Period17/07/2319/07/23

Keywords

  • Classification
  • Convolutional Neural Network
  • MRI
  • VGG-19

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