Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (6): 11-16.doi: 10.16180/j.cnki.issn1007-7820.2021.06.002

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Staging Diagnosis of Liver Fibrosis Based on Transfer Learning Strategy

ZHAI Yuexian1,LIU Xiang1,SONG Jialin2   

  1. 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science,Shanghai 201620,China
    2. Department of Ultrasound Diagnosis and Treatment,Changzheng Hospital,Second Military Medical University of Chinese People’s Liberation Army,Shanghai 200003,China
  • Received:2020-02-23 Online:2021-06-15 Published:2021-06-01
  • Supported by:
    Natural Science Foundation of Shanghai(19ZR1421500)


In view of the problem that the accuracy of classification of liver fibrosis into four stages is low, and the difficulties in distinguishing S2 and S3 stages, a method of liver fibrosis’s diagnosis based on transfer learning strategy is proposed in this study. This method is based on a pre-trained deep residual network model, randomly initializes the weight parameters of each layer, and adds a dataset expanded by the method of rotation and cutting patch blocks to fine-tune various parameters. After the softmax classifier combined with the patch block voting principle, the accuracy rates of liver fibrosis S0~S1S2S3 and S4 stages are 93.75%、90.63%、87.50% and 86.96%, respectively. The proposed method achieves good results in the quantitative diagnosis of liver fibrosis based on high-frequency ultrasound images. The comparative analysis reveales that this method is superior to other existing methods, and provides a more effective solution for clinical computer-aided diagnosis of liver fibrosis diseases.

Key words: liver fibrosis, transfer learning, deep residual network, fine-tuning, voting principle, softmax classifier, high-frequency ultrasound, computer-aided diagnosis

CLC Number: 

  • TP181