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

Table 1 Comparison between different types of machine learning approaches used in studies focused on polyp detection and classification

Characteristics	Support vector machine	Random forest	Decision trees	Deep neural networks	Context	Ref.
High dimensional data	High	High	Moderate	High	Performance	Shen et al[12]; Goodfellow et al[26]
Overlapped classes	Low	Low	Low	High
Imbalance datasets	Moderate	High	Low	Moderate
Non-linear data	Moderate	High	Moderate	High
Larger dataset	Moderate1	High1	Low	High
Outliers	Moderate	Moderate	Low	High	Robustness	Shen et al[12]; Yu et al[20]
Over-fitting	Moderate	High	Low	High
Handling of missing values	Poor	Good	Good	Good
Reproducibility	High	High	High	Moderate	Complexity	Yu et al[20]
Interpretability	Moderate	Moderate	High	Low

¹Consider that it leads to slow response time.

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.

Table 3 Summary of studies focused on artificial intelligence applications for automatic polyp detection, classification, and segmentation

Study	Screening test	Imaging modality	Data type	AI-based algorithm	Contribution	Acc	Sen	Spe
Wimmer et al[46]	Colonoscopy	WL, NBI	Images	k-nearest neighbours	Polyp classification: non-neoplastic, neoplastic	80%	-	-
Tajbakhsh et al[22]	Colonoscopy	WL	Images	Decision trees; Random forest	Automatic polyp detection	-	88%	-
Hu et al[21]	CT Colonography	Greyscale	Images	Random forest	Polyp classification: non-neoplastic, neoplastic	-	-	-
Zhang et al[50]	Colonoscopy	WL, NBI	Images	CNN: Caffenet	Polyp detection and classification: benign from malignant	86%	88%	-
Shin et al[23]	Colonoscopy	WL	Images	Support vector machine	Whole image classification: polyps from non-polyps	96%	96%	96%
Sánchez-González et al[32]	Colonoscopy	WL	Images	Random forest; CNN: Bayesnet	Polyp segmentation	97%	76%	99%
Tan et al[52]	CT Colonography	Greyscale	Images	Customized CNN	Polyp classification: adenoma from adenocarcinoma	87%	90%	71%
Fonolla et al[51]	Colonoscopy	WL, NBI, LCI	Images	CNN: EfficientNet	Polyp classification: benign from pre-malignant	95%	96%	93%
Hwang et al[46]	Colonoscopy	WL	Images	Customized CNN	Polyp detection and segmentation	-	-	-
Park et al[53]	Colonoscopy	WL	Images	Customized CNN	Whole image classification: normal, adenoma and adenocarcinoma	94%	~94%	-
Viscaino et al[54]	Colonoscopy	Greyscale	Images	Support vector machine; Decision treesk-nearest neighbours; Random forest	Whole image classification: polyp and non-polyp	97%	98%	96%

WL: White light; NBI: Narrow-band imaging; LCI: Linked colour imaging; Acc: Accuracy; Sen: Sensitivity; Spe: Specificity.

Table 4 Summary of publicly available colonoscopy datasets

Dataset	Year	Description	Data type	Ground truth
CVC-ColonDB[29,58]	2012	380 sequential WL images from 15 videos	Images (574 × 500 pixels)	Binary mask to locate the polyp
CVC-PolypHD[58,59]	2012	56 WL images	Images (1920 × 1080 pixels)	Binary mask to locate the polyp
ETIS-Larib[55]	2014	196 WL images from 34 video sequences (44 different polyps)	Images (1125 × 966)	Binary mask to locate the polyp
CVC-ClinicDB[37]	2015	612 sequential WL images from 31 videos sequences (31 different polyps)	Images (388 × 284 pixels)	Binary mask to locate the polyp
ASU-Mayo[22]	2016	38 short video sequences (NBI, WL)	Video (SD and HD video)	Binary mask for 20 training videos
Colonoscopic dataset[49]	2016	76 short video sequences (NBI, WL)	Video	Labels: hyperplastic, adenoma and serrated
Kvasir-SEG[60]	2017	1000 images with polyps	Images	Binary mask to locate the polyp
CVC-ClinicVideoDB[61]	2017	18 sequences	Video (SD video)	Binary mask to locate the polyp
CP-CHILD-A, CP-CHILD-B[62]	2020	10000 images	Images (256 × 256)	Labels: polyp and non-polyp

WL: White light; NBI: Narrow-band imaging.