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A Disease Diagnostic Assistant System Using DTI and Extreme Learning Machine

Shuqiang Wang, Dewei Zeng, Yanyan Shen, and Jinxing Hu
Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Science
Abstract—There is a growing interest in applying diffusion tensor imaging (DTI) to the evaluation of brain and spinal cord related disease. In the present study, the DTI data and extreme learning machine were employed to identify the levels with cervical spondylotic myelopathy (CSM) in spinal cord. In this work, there are 40 volunteers including 20 healthy people and 20 patients with CSM ranging from 24 to 81 years old. Experiment results show that the extreme learning machine based classifier performs well in detecting the patients with CSM (accuracy 93.6%, sensitivity: 91.2%, specificity 94.7%). The current work reveals the potential of using diffusion tensor imaging in conjunction with extreme learning machine to automate the classification of healthy subjects and subjects with brain and spinal cord related disease.

Index Terms—diagnostic assistant system, extreme learning machine, diffusion tensor imaging, cervical spondylotic mye-lopathy

Cite: Shuqiang Wang, Dewei Zeng, Yanyan Shen, and Jinxing Hu, "A Disease Diagnostic Assistant System Using DTI and Extreme Learning Machine," Vol. 7, No. 2, pp. 129-133, May, 2016. doi: 10.12720/jait.7.2.129-133
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