Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Alzheimer's disease (AD) is a progressive brain disease that causes a different pattern of brain atrophy from normal aging. Early identification of AD is crucial since the progression of the disease can be slowed down by medication. In the field of image recognition, its accuracy has been significantly improved by using convolutional neural networks (CNNs). Similarly, in the field of medical image processing, researches on the diagnostic support using CNN have been studied. In this paper, we propose an AD classification method using CNN, inspired by the success of CNNs in brain age estimation. Through experiments using a large-scale database, we demonstrate the effectiveness of our proposed method.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2021
EditorsRuey-Feng Chang
PublisherSPIE
ISBN (Electronic)9781510644205
DOIs
Publication statusPublished - 2021
EventInternational Forum on Medical Imaging in Asia 2021, IFMIA 2021 - Taipei, Taiwan, Province of China
Duration: 2021 Jan 242021 Jan 26

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11792
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2021, IFMIA 2021
Country/TerritoryTaiwan, Province of China
CityTaipei
Period21/1/2421/1/26

Keywords

  • Alzheimer's disease
  • brain age estimation
  • brain MRI image

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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