TY - GEN
T1 - Deep 3D Convolutional Neural Network Architectures for Alzheimer’s Disease Diagnosis
AU - Karasawa, Hiroki
AU - Liu, Chien-Liang
AU - Ohwada, Hayato
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - Dementia has become a social problem in the aging society of advanced countries. Currently, 46.8 million people have dementia worldwide, and that figure is predicted to increase threefold to 130 million people by 2050. Alzheimer’s disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase dramatically in the future, due to the increasing number of patients as a result of the aging society. However, it is still very difficult to cure AD; thus, the detection of AD is crucial. This study proposes the use of machine learning to detect AD using brain image data, with the goal of reducing the cost of diagnosing and caring for AD patients. Most machine learning algorithms rely on good feature representations, which are commonly obtained manually and require domain experts to provide guidance. Feature extraction is a time-consuming and labor-intensive task. In contrast, the 3D Convolutional Neural Network (3DCNN) automatically learns feature representation from images and is not greatly affected by image processing. However, the performance of CNN depends on its layer architecture. This study proposes a novel 3DCNN architecture for MRI image diagnosis of AD.
AB - Dementia has become a social problem in the aging society of advanced countries. Currently, 46.8 million people have dementia worldwide, and that figure is predicted to increase threefold to 130 million people by 2050. Alzheimer’s disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase dramatically in the future, due to the increasing number of patients as a result of the aging society. However, it is still very difficult to cure AD; thus, the detection of AD is crucial. This study proposes the use of machine learning to detect AD using brain image data, with the goal of reducing the cost of diagnosing and caring for AD patients. Most machine learning algorithms rely on good feature representations, which are commonly obtained manually and require domain experts to provide guidance. Feature extraction is a time-consuming and labor-intensive task. In contrast, the 3D Convolutional Neural Network (3DCNN) automatically learns feature representation from images and is not greatly affected by image processing. However, the performance of CNN depends on its layer architecture. This study proposes a novel 3DCNN architecture for MRI image diagnosis of AD.
KW - 3D Convolutional Neural Network
KW - Alzheimer’s disease diagnosis
KW - Deep residual network
KW - Image processing
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85043537423&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75417-8_27
DO - 10.1007/978-3-319-75417-8_27
M3 - Conference contribution
AN - SCOPUS:85043537423
SN - 9783319754161
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 287
EP - 296
BT - Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings
A2 - Pham, Hoang
A2 - Nguyen, Ngoc Thanh
A2 - Trawinski, Bogdan
A2 - Hoang, Duong Hung
A2 - Hong, Tzung-Pei
PB - Springer Verlag
T2 - 10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018
Y2 - 19 March 2018 through 21 March 2018
ER -