Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211200097-6.doi: 10.11896/jsjkx.211200097

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Thymoma CT Image Prediction Method Based on Deep Learning and Improved Extreme Learning Machine Ensemble Learning

XU Kun-cai1, FENG Bao2, CHEN Ye-hang2, LIU Yu2, ZHOU Hao-yang2, CHEN Xiang-meng3   

  1. 1 School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 School of Electronic Information and Automation,Guilin University of Aerospace Technology,Guilin,Guangxi 541004,China
    3 Medical Image Intelligent Computing and Application Laboratory,Jiangmen Central Hospital,Jiangmen,Guangdong 529000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:XU Kun-cai,born in 1997,postgra-duate.His main research interests include application of machine learning in medical images and so on.
    CHEN Ye-hang,born in 1993,master.His main research interests include machine learning technology and its application in biomedical signal processing.
  • Supported by:
    National Natural Science Foundation of China(81960324) and General Project of Guangxi Natural Science Foundation(Guangdong Guangxi Joint Fund Project)(2021GXNSFAA075037).

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Abstract: To predict the risk of thymoma patients before operation,a computer-aided analysis method combining deep learning and extreme learning machine ensemble learning is proposed.Firstly,the CT image of thymoma is transformed to different scales by wavelet multi-scale transform,and the wavelet energy map is calculated to improve the richness and diversity of image information.Secondly,the convolution neural network model is trained by wavelet energy map,and the specific depth features related to tasks in wavelet energy map are extracted by convolution kernel.Finally,the differentiated training subsets are trained based on the improved limit learning machine,and ensemble learning is constructed to improve the stability and prediction accuracy of the model.Based on multicenter experiments,the results show that the proposed method has good generalization performance and stability.The AUCs of the three verification sets are 0.833,0.771 and 0.784 respectively.

Key words: Thymoma, Wavelet transform, Convolutional neural network, Extreme learning machine, Ensemble learning

CLC Number: 

  • TN911.73-34
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