JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (7): 60-66.doi: 10.6040/j.issn.1671-9352.4.2022.2945

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Prediction method of IDH1 mutation status of glioma based on improved EfficientNetV2

Huachang XU1(),Qian XU2,Yulin ZHAO1,Fengning LIANG1,Kai XU2,Hong ZHU1,*()   

  1. 1. School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou 221000, Jiangsu, China
    2. Medical Imaging Department, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, Jiangsu, China
  • Received:2022-07-26 Online:2023-07-20 Published:2023-07-05
  • Contact: Hong ZHU E-mail:300109110841@stu.xzhmu.edu.cn;zhuhong@xzhmu.edu.cn

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Abstract:

The paper proposes a method for predicting the IDH1 mutation status of gliomas based on improved EfficientNetV2. Firstly, k-means clustering algorithm is used to label pseudo-labels for unlabeled glioma MRI data, and the Vision Transformer network is used to correct the pseudo-labels to realize glioma data amplification. Secondly, the coordinate attention is added to the EfficientNetV2 model, and the fruit fly optimization algorithm is used to give the optimal weight to the pseudo-label data during the model training process. Finally, the glioma IDH1 mutation status is classified, and the prediction accuracy is 96.32%. The experimental results show that the proposed method can non-invasively and accurately predict the IDH1 mutation status of gliomas before surgery.

Key words: EfficientNetV2, glioma IDH1, k-means clustering, pseudo-label, fruit fly optimization algorithm

CLC Number: 

  • TP18

Fig.1

k-means clustering algorithm flowchart"

Fig.2

Pseudo-label labeling flowchart"

Table 1

EfficientNetV2-S network structure"

Stage Operator Stride Channels Layers
0 Conv3×3 2 24 1
1 Fused-MBConv1, k3×3 1 24 2
2 Fused-MBConv4, k3×3 2 48 4
3 Fused-MBConv4, k3×3 2 64 4
4 MBConv4, k3×3, SE0.25 2 128 6
5 MBConv6, k3×3, SE0.25 1 160 9
6 MBConv6, k3×3, SE0.25 2 256 15
7 Conv1×1 & Pooling & FC — 1 280 1

Fig.3

MBConv module in EfficientNetV2"

Fig.4

Improved MBConv module"

Fig.5

Fruit fly optimization algorithm process"

Table 2

Model test results"

模型 准确率/% 精确率/% 召回率/% F1分数/% AUROC
T-Net 91.12 89.81 90.71 90.23 0.970 8
TP-Net 94.54 93.61 91.16 92.30 0.976 1
TP-CA-Net 96.32 95.16 94.70 94.93 0.979 2

Fig.6

ROC curves of the three models"

Table 3

Performance comparison of different models in predicting IDH1 status in TCIA gliomas"

指标 Inception-V3 Mobile-Net-V2 ResNet-50 DenseNet-121 EfficientNet-B0 TP-CA-Net
准确率/% 88.73 77.93 0.892 0 86.85 89.67 95.87
精确率/% 68.19 46.56 44.60 64.25 44.84 95.67
召回率/% 71.37 45.25 50.00 64.25 50.00 92.36
F1分数/% 69.59 45.81 47.15 64.25 47.28 93.88
AUROC 0.832 9 0.513 3 0.535 5 0.866 6 0.530 5 0.972 6
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