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Deep 3D Convolutional Neural Network Architectures for Alzheimer’s Disease Diagnosis

  • Hiroki Karasawa
  • Chien-Liang Liu
  • Hayato Ohwada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10751)

Abstract

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.

Keywords

Alzheimer’s disease diagnosis 3D Convolutional Neural Network Deep residual network Image processing Machine learning 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hiroki Karasawa
    • 1
  • Chien-Liang Liu
    • 2
  • Hayato Ohwada
    • 1
  1. 1.Department of Industrial AdministrationTokyo University of ScienceTokyoJapan
  2. 2.Department of Industrial Engineering and ManagementNational Ciao Tung UniversityHsinchuTaiwan

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