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. 2021 Apr 28;17(1):5–11. doi: 10.17925/EE.2021.17.1.5

Table 2: Comparative performance of different networks for diabetic foot ulcer detection on different intersection-over-union thresholds.

Method IoU ≥0.5 IoU ≥0.6 IoU ≥0.7 IoU ≥0.8 IoU ≥0.9
F1 mAP F1 mAP F1 mAP F1 mAP F1 mAP
FRCNN R-FCN 0.6784 0.6596 0.6044 0.5618 0.4829 0.4044 0.2705 0.1487 0.0534 0.009
FRCNN ResNet101 0.6623 0.6518 0.5931 0.5661 0.4701 0.4087 0.2703 0.1689 0.0551 0.0112
FRCNN Inc-Res 0.6716 0.6462 0.5902 0.5385 0.4592 0.3827 0.2616 0.1644 0.0483 0.0095
YOLOv5 0.6612 0.6304 0.5898 0.5353 0.4418 0.3420 0.2355 0.1175 0.0383 0.0046
EffDet 0.6929 0.6216 0.6076 0.5143 0.4710 0.3503 0.2505 0.2167 0.0343 0.0031

EffDet = EfficientDet; F1 = harmonic mean of precision and recall; FRCNN Inc-Res = faster region-based convolutional neural network Inception-v2-ResNet101; IoU = intersection over union; mAP = mean average precision; R-FCN = region-based fully convolutional network; ResNet = residual neural network; YOLOv5 = You Only Look Once version 5.