眼底病变OCT图像的轻量化识别算法
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侯小虎,贾晓芬,赵佰亭
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Lightweight recognition algorithm for OCT images of fundus lesions
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Xiao-hu HOU,Xiao-fen JIA,Bai-ting ZHAO
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| 表 4 不同模型在NEH数据集上的实验对比结果 |
| Tab.4 Experimental results of performance comparison of different models in NEH dataset |
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| 模型 | 图片类别 | P/% | R/% | Spe/% | Acc/% | NP/106 | OAcc/% | | FPN+VGG16[11] | — | — | — | 96.5±0.8 | — | — | 93.4±1.4 | | Resnet18[12] | CNV | 97.13 | 95.40 | 99.30 | 98.50 | 11.18 | 94.01±0.4 | | 玻璃膜疣 | 91.47 | 88.77 | 96.73 | 94.37 | | 正常眼底 | 94.30 | 96.53 | 93.70 | 95.16 | | MB-CNN | CNV | 96.8 | 96.1 | 99.2 | 98.6 | 1.35 | 94.37±0.4 | | 玻璃膜疣 | 91.7 | 89.6 | 96.6 | 94.6 | | 正常眼底 | 94.9 | 96.4 | 94.6 | 95.5 |
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