Chinese Journal of Magnetic Resonance ›› 2025, Vol. 42 ›› Issue (1): 56-66.doi: 10.11938/cjmr20243119cstr: 32225.14.cjmr20243119
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XUE Peiyang1, GENG Chen2, LI Yuxin3, BAO Yifang3, LU Yucheng3,#(), DAI Yakang1,2,*(
)
Received:
2024-06-17
Published:
2025-03-05
Online:
2024-08-26
Contact:
#Tel: 18679050077, E-mail: 359918717@qq.com;*Tel: 15850168495, E-mail: daiyk@sibet.ac.cn.
CLC Number:
XUE Peiyang, GENG Chen, LI Yuxin, BAO Yifang, LU Yucheng, DAI Yakang. A Classification Method for Cerebral Aneurysms in TOF-MRA Based on Improved 3D ResNet50 Model[J]. Chinese Journal of Magnetic Resonance, 2025, 42(1): 56-66.
Fig. 3
PMAF-Net architecture diagram. The top part shows the overall network structure, while the bottom part displays the MDC module (A) and the CSA modules (B and C). The numbers at the bottom of the residual blocks indicate the size and number of channels of the feature map after each downsampling (e.g., 323×64 indicates a feature map size of 32×32×32 with 64 output channels)
Table 4
Classification performance of PMAF-Net on different groups based on maximum diameter of cerebral aneurysms
亚组分析 Subgroup analysis | 准确率 Accuracy | 召回率 Recall | 精确率 Precision | F1分数 F1_Score |
---|---|---|---|---|
总体性能 | 83.97% | 84.48% | 80.33% | 0.8235 |
按最大径划分 | ||||
小型动脉瘤(47) | 80.85% | 77.78% | 50.00% | 0.6087 |
中型动脉瘤(81) | 85.19% | 84.78% | 88.64% | 0.8667 |
大型动脉瘤(3) | 100.00% | 100.00% | 100.00% | 1.0000 |
Fig. 6
ROC curves of PMAF-Net compared with other network models. The black diagonal line represents the performance of a completely randomized classifier, and the rest are the ROC curves for ResNet18 (red), ResNet50 (green), DenseNet (blue), MobileNet (orange), EfficientNet (purple), and PMAF-Net (brown), respectively
Table 5
Comparison of the classification performance of the proposed method with existing methods on the test set
模型 Models | 准确率 Accuracy | 召回率 Recall | 精确率 Precision | F1分数 F1_Socre |
---|---|---|---|---|
ResNet18[ | 74.81% | 60.34% | 77.78% | 0.6796 |
ResNet50[ | 72.52% | 74.14% | 67.19% | 0.7049 |
DenseNet[ | 67.94% | 68.97% | 62.50% | 0.6557 |
MobileNet[ | 77.86% | 81.03% | 72.31% | 0.7642 |
EfficientNet[ | 72.52% | 65.52% | 70.37% | 0.6786 |
PMAF-Net(本文) | 83.97% | 84.48% | 80.33% | 0.8235 |
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