基于3D ResNet50改进模型的TOF-MRA脑动脉瘤分类方法

薛培阳, 耿辰, 李郁欣, 鲍奕仿, 鲁宇澄, 戴亚康

A Classification Method for Cerebral Aneurysms in TOF-MRA Based on Improved 3D ResNet50 Model

XUE Peiyang, GENG Chen, LI Yuxin, BAO Yifang, LU Yucheng, DAI Yakang

表5 本文方法与现有方法在测试集上的分类性能比较

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[23] 74.81% 60.34% 77.78% 0.6796 ResNet50[20] 72.52% 74.14% 67.19% 0.7049 DenseNet[26] 67.94% 68.97% 62.50% 0.6557 MobileNet[27] 77.86% 81.03% 72.31% 0.7642 EfficientNet[28] 72.52% 65.52% 70.37% 0.6786 PMAF-Net（本文） 83.97% 84.48% 80.33% 0.8235