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

Daiki Endo; Koichi Ito; Takafumi Aoki

doi:10.1117/12.2590826

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	International Forum on Medical Imaging in Asia 2021
Editors	Ruey-Feng Chang
Publisher	SPIE
ISBN (Electronic)	9781510644205
DOIs	https://doi.org/10.1117/12.2590826
Publication status	Published - 2021
Event	International Forum on Medical Imaging in Asia 2021, IFMIA 2021 - Taipei, Taiwan, Province of China Duration: 2021 Jan 24 → 2021 Jan 26

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11792
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	International Forum on Medical Imaging in Asia 2021, IFMIA 2021
Country/Territory	Taiwan, Province of China
City	Taipei
Period	21/1/24 → 21/1/26

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

Access to Document

10.1117/12.2590826

Cite this

Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation. / Endo, Daiki; Ito, Koichi ; Aoki, Takafumi.

International Forum on Medical Imaging in Asia 2021. ed. / Ruey-Feng Chang. SPIE, 2021. 117920E (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11792).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Endo, D, Ito, K & Aoki, T 2021, Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation. in R-F Chang (ed.), International Forum on Medical Imaging in Asia 2021., 117920E, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11792, SPIE, International Forum on Medical Imaging in Asia 2021, IFMIA 2021, Taipei, Taiwan, Province of China, 21/1/24. https://doi.org/10.1117/12.2590826

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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.",

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AU - Aoki, Takafumi

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N2 - 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.

AB - 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.

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

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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