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http://dx.doi.org/10.9717/kmms.2020.23.8.940

Using 3D Deep Convolutional Neural Network with MRI Biomarker patch Images for Alzheimer's Disease Diagnosis  

Yun, Joo Young (Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
Kim, Kyeong Tae (Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
Choi, Jae Young (Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 940-952 More about this Journal
Abstract
The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.
Keywords
Alzheimer's Disease; Biomarker; Hippocampus; Cerebrospinal Fluid; 3D Convolutional Neural Network; Score Fusion;
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Times Cited By KSCI : 2  (Citation Analysis)
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