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

20 April 2021 Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation

Daiki Endo, Koichi Ito, Takafumi Aoki

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920E (2021) https://doi.org/10.1117/12.2590826
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

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.

Citation Download Citation

Daiki Endo, Koichi Ito, and Takafumi Aoki "Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920E (20 April 2021); https://doi.org/10.1117/12.2590826

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