Table 2.

Summary of adopted evaluation metrics.

Metrics	Formulas	Description
Accuracy (ACC)	$\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+ \mathrm{FP}+\mathrm{FN}}$	The ratio of correctly predicted pixels to the total number of pixels in the processed image.
Precision (Pc)	$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$	The ratio of correctly predicted lesion pixels to the total of expected lesion pixels.
Sensitivity (Sen)	$\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}$	The ratio of the correctly predicted lesion pixels to the total number of real lesion pixels.
F1 score (F1)	$2.\frac{\mathrm{Precision} . \mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}$	The ratio obtained from a combination of both precision and sensitivity results.
Specificity (Sp)	$\frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FP}}$	The ratio of correctly predicted normal pixels to the total number of actual normal pixels.
Dice coefficient (DC)	$\frac{2.\left(\mathrm{X}\cap \mathrm{Y}\right)}{\mathrm{X}+\mathrm{Y}}$	The similarity between the method output (Y) and the ground truth (X).
Structural metric (Sm)	Sm = (1 − β).Sos(Sop,Sgt) + β.Sor(Sop,Sgt)	The structural similarity between the prediction map and ground truth mask.
Mean Absolute Error (MAE)	$\mathrm{MAE}=\frac{1}{\mathrm{w} . \mathrm{h}}$ ∑wi∑hj|Sop(i,j) − Sgt(i,j)|	Measures the pixel-wise difference.