基于迁移学习策略的肝纤维化分期诊断方法

doi:10.16180/j.cnki.issn1007-7820.2021.06.002

Abstract

Abstract:

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~S₁、S₂、S₃ 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

ZHAI Yuexian,LIU Xiang,SONG Jialin. Staging Diagnosis of Liver Fibrosis Based on Transfer Learning Strategy[J].Electronic Science and Technology, 2021, 34(6): 11-16.

Figures/Tables 10

Figure 1.

Figure 2.

Figure 3.

Table 1

Structure of ResNet-34 network"

层次名称	输出大小	34-layer
Conv1	32×32	7×7, 64, stride 2
Conv2_x	16×16	3×3 max pool, stride 2 $3 × 3,64 3 × 3,64$
Conv3_x	8×8	$3 × 3,128 3 × 3,128$
Conv4_x	4×4	$3 × 3,256 3 × 3,256$
Conv5_x	2×2	$3 × 3,512 3 × 3,512$
-	1×1	global poling, 4-d fc softmax

Table 1

Figure 4.

Figure 5.

Figure 6.

Table 2

Table 3

Table 4

Comparative test of classification performance of other network models"

方法名称	特点	肝纤维化阶段
方法名称	特点	S₀~ $S 1 /$ %	S₂/%	S₃/%	S₄/%
纹理分析法	GLCM参数+ SVM	62.50	68.75	66.67	73.91
文献[16]	肝包膜特征+ KNN/TSVM	68.75	62.50	54.17	65.22
ResNet-18	残差网络结构	87.50	84.34	83.33	86.96
本文方法	残差网络结构	93.75	90.63	87.50	86.96

Table 4

References 16

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肝纤维化	预测类别
肝纤维化	S₀~S₁期	S₂期	S₃期	S₄期
S₀~S₁期	15	1	0	0
S₂期	1	29	1	1
S₃期	0	2	21	1
S₄期	0	1	2	20

肝纤维化	分类性能
肝纤维化	特异度/%	灵敏度/%	F₁-score/%
S₀~S₁期	93.75	93.75	93.75
S₂期	87.88	90.63	89.23
S₃期	87.50	87.50	87.50
S₄期	90.91	86.96	88.90

Staging Diagnosis of Liver Fibrosis Based on Transfer Learning Strategy

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