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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