TY - GEN
T1 - Deep Learning Approach to Predict Alzheimer's Disease through Magnetic Resonance Images
AU - Ramadhan, Gilang Titah
AU - Pusparani, Yori
AU - Farady, Isack
AU - Furqon, Elvin Nur
AU - Lin, Chih Yang
AU - Chao, Wen Hung
AU - Rani Alex, John Sahaya
AU - Aparajeeta, Jeetashree
AU - Lung, Chi Wen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Classification
KW - Convolutional Neural Network
KW - MRI
KW - VGG-19
UR - http://www.scopus.com/inward/record.url?scp=85174915804&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226688
DO - 10.1109/ICCE-Taiwan58799.2023.10226688
M3 - 會議論文篇章
AN - SCOPUS:85174915804
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 845
EP - 846
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -
