Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Table 2 Most common evaluation metrics found in the state of the art for detection, segmentation and classification tasks

Term	Symbol	Description
Positive	P	Number of real positive cases in the data
Negative	N	Number of real negative cases in the data
True positive	TP	Number of correct positive cases classified/detected
True negative	TN	Number of correct negative cases classified/detected
False positive	FP	Instances incorrectly classified/detected as positive
False negative	FN	Instances incorrectly classified/detected as negative
Area under curve	AUC	Area under the ROC plot
Term	Task	Formulation
Accuracy	C, D, S	(TP + TN)/(TP + TN + FN + FP)
Precision/PPV	C, D, S	TP/(TP + FP)
Sensitivity/Recall/TPR	C, D, S	TP/(TP + FN)
Specificity/TNR	C, D, S	TN/(TN + FP)
FPR	C, D, S	FP/(TN + FP)
FNR	C, D, S	FN/(TP + FN)
f1-score/DICE index	C, D, S	2 ∙ (precision ∙ recall)/(precision + recall)
f2-score	C, D, S	4 ∙ (precision∙recall)/(4∙precision + recall)
IoU/Jaccard index	D, S	(target ∩ prediction)/(target ∪ prediction)
AAC	D, S	(detected area ∩ real area)/(real area)

C: Classification; D: Detection; S: Segmentation. PPV: Positive predictive value; TPR: True positive rate; TNR: True negative rate. AAC: Annotated area covered.