NDLI: Early diagnosis of Alzheimer's disease with deep learning

Content Provider	IEEE Xplore Digital Library
Author	Siqi Liu Sidong Liu Weidong Cai Pujol, S. Kikinis, R. Feng, D.
Copyright Year	2014
Description	Author affiliation: BMIT Res. Group, Univ. of Sydney, Sydney, NSW, Australia (Siqi Liu; Sidong Liu; Weidong Cai; Feng, D.) \|\| Med. Sch., Brigham & Women's Hosp., Surg. Planning Lab., Harvard Univ., Boston, MA, USA (Pujol, S.; Kikinis, R.)
Abstract	The accurate diagnosis of Alzheimer's disease (AD) plays a significant role in patient care, especially at the early stage, because the consciousness of the severity and the progression risks allows the patients to take prevention measures before irreversible brain damages are shaped. Although many studies have applied machine learning methods for computer-aided-diagnosis (CAD) of AD recently, a bottleneck of the diagnosis performance was shown in most of the existing researches, mainly due to the congenital limitations of the chosen learning models. In this study, we design a deep learning architecture, which contains stacked auto-encoders and a softmax output layer, to overcome the bottleneck and aid the diagnosis of AD and its prodromal stage, Mild Cognitive Impairment (MCI). Compared to the previous workflows, our method is capable of analyzing multiple classes in one setting, and requires less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments.
Starting Page	1015
Ending Page	1018
File Size	782825
Page Count	4
File Format	PDF
ISBN	9781467319614
DOI	10.1109/ISBI.2014.6868045
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Alzheimer's disease Training Feature extraction Support vector machines Magnetic resonance imaging Neurons classification neuroimaging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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