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Abstract #0027

A deep learning framework with redundancy removal and its diagnostic performance of Parkinson's disease

Fan Huang1, Mingyi Zhou2, Shi-ming Wang1, Jing Wu2, Liaqat Ali2, Yi-Hsin Weng3, Yao-Liang Chen4, Jiun-Jie Wang1, and Yipeng Liu2

1Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan, 2School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China, 3Neurology, Chang Gung Memorial Hospital, Taoyuan, Taiwan, 4Diagnostic Radiology, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

Computer-aided diagnosis using deep learning methods shows its potential in medical images classifications. This study aims to examine the diagnostic performance of diffusion tensor imaging using a 4-steps framework for deep learning to differentially diagnose patients with Parkinson's disease(PD) and normal controls(NC).

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