眼底病变OCT图像的轻量化识别算法

侯小虎,贾晓芬,赵佰亭

Lightweight recognition algorithm for OCT images of fundus lesions

Xiao-hu HOU,Xiao-fen JIA,Bai-ting ZHAO

表 2 不同模型在UCSD数据集上的性能对比实验结果

Tab.2 Experimental results of performance comparison of different models in UCSD dataset

模型 图片类别 P/% R/% Spe/% Acc/% NP/106 OAcc/% LACNN[5] CNV 93.5 89.8 95.1 92.7 — 90.1±1.2 DME 86.4 87.5 98.0 96.6 玻璃膜疣 70.0 72.5 95.9 93.6 正常眼底 94.8 97.3 97.4 97.4 Multi-Label CNN[6] CNV 93.5 88.1 96.0 93.8 — 90.4±1.2 DME 83.1 86.0 95.8 96.5 玻璃膜疣 69.8 72.1 96.8 93.3 正常眼底 95.1 96.1 97.9 96.8 PCAM[7] — 91.52 91.22 — — 11.09 94.15±1.15 Resnet18[12] CNV 97.83 97.63 98.27 97.97 11.18 96.52±0.5 DME 95.30 95.17 99.27 98.70 玻璃膜疣 89.87 90.70 98.80 97.97 正常眼底 97.43 97.43 98.83 98.4 DMF-CNN[8] CNV 97.05 97.33 — — — 94.37 DME 96.26 93.22 94.64 玻璃膜疣 87.73 98.29 94.43 正常眼底 97.49 97.62 96.03 MB-CNN CNV 98.39 98.49 98.60 98.51 1.35 97.22±0.6 DME 97.30 96.27 99.49 99.07 玻璃膜疣 92.24 90.84 99.12 98.33 正常眼底 97.77 98.20 98.63 98.75