@inproceedings{4e21eae141c742008c33c8d626618207,
title = "Performance improvement of Alzheimer's disease classification inspired by CNN in brain age estimation",
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.",
keywords = "Alzheimer's disease, brain age estimation, brain MRI image",
author = "Daiki Endo and Koichi Ito and Takafumi Aoki",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; International Forum on Medical Imaging in Asia 2021, IFMIA 2021 ; Conference date: 24-01-2021 Through 26-01-2021",
year = "2021",
doi = "10.1117/12.2590826",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ruey-Feng Chang",
booktitle = "International Forum on Medical Imaging in Asia 2021",
address = "United States",
}