轻量化AD-Net模型用于颅内肿瘤MRI图像的分类研究

向朝, 随力, 张昊天, 段梦雨, 刘卓睿

A Lightweight AD-Net Model for the Classification of Intracranial Tumors in MRI Images

XIANG Zhao, SUI Li, ZHANG Haotian, DUAN Mengyu, LIU Zhuorui

表1 对比实验结果

Table 1 Comparative experimental results

模型名称 准确率(Accuracy) 精确率(Precision) 召回率(Recall) 特异性(Specifity) F1分数(F1 Score) Self-ONN4L1DN 0.9390±0.0129 0.9348±0.0133 0.9377±0.013 0.9434±0.0125 0.9350±0.0133 Self-ONN4L 0.9376±0.0130 0.9356±0.0132 0.9376±0.0130 0.9428±0.0125 0.9375±0.0131 Self-ONN6L 0.9419±0.0126 0.9485±0.0119 0.9429±0.0125 0.9547±0.0112 0.9458±0.0122 Self-ONN6L1DN 0.9650±0.0112 0.9553±0.0111 0.9520±0.0115 0.9611±0.0104 0.9531±0.0114 Vanilla CNN8L 0.9295±0.0138 0.9289±0.0139 0.9292±0.0138 0.9395±0.0129 0.9278±0.0140 Vanilla CNN6L 0.9283±0.0139 0.9276±0.0140 0.9290±0.0139 0.9387±0.0129 0.9245±0.0143 DenseNet201 0.9458±0.0122 0.9455±0.0122 0.9428±0.0125 0.9584±0.0108 0.9480±0.0120 ResNet101 0.9490±0.0107 0.9596±0.0106 0.9589±0.0107 0.9589±0.0107 0.9586±0.0107 AD-Net 0.9747±0.0092 0.9693±0.0093 0.9685±0.0094 0.9795±0.0076 0.9683±0.0095