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http://dx.doi.org/10.9717/kmms.2019.22.10.1168

Alzheimer's Disease Classification with Automated MRI Biomarker Detection Using Faster R-CNN for Alzheimer's Disease Diagnosis  

Son, Joo Hyung (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.22, no.10, 2019 , pp. 1168-1177 More about this Journal
Abstract
In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.
Keywords
Computer-Aided Diagnosis; Alzheimer's Disease; Deep Convolution Neural Network; Faster R-CNN; Biomarker;
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Times Cited By KSCI : 1  (Citation Analysis)
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