Automated detection of nonmelanoma skin cancer using digital images: a systematic review

Marka, Arthur; Carter, Joi B.; Toto, Ermal; Hassanpour, Saeed

doi:10.1186/s12880-019-0307-7

BMC Medical Imaging

Table 2 Overview of literature search

From: Automated detection of nonmelanoma skin cancer using digital images: a systematic review

Source	Target NMSC	Digital Image Modality	Database	Algorithm	Outcome	Quality Rating^a
Abbas, 2016 [14]	BCC; CSCC	Dermoscopic	30 BCCs, 30 CSCCs, 300 various other lesions (30% of dataset used for training and 70% for testing)^b	ANN	AUROC: 0.92, (BCC), 0.95 (CSCC); Sensitivity: 97% (BCC), 97% (CSCC); Specificity: 68% (BCC), 80% (CSCC)	5
Ballerini, 2012 [43]	BCC; CSCC	Non-dermoscopic	239 BCCs, 88 CSCCs, 633 benign lesions (3-fold cross-validation)	k-NN^c	Accuracy: 89.7%^d; Sensitivity: 89.9%^d; Specificity: 89.6%^d	3
Chang 2013 [15]	BCC; CSCC	Non-dermoscopic	110 BCCs, 20 CSCCs, 639 various other lesions (leave-one-out cross-validation)^b	MSVM	Sensitivity: 90% (BCC), 80% (CSCC)	2
Cheng, 2011 [34]	BCC	Dermoscopic	59 BCCs, 152 benign lesions (leave-one-out cross-validation)	ANN	AUROC: 0.967	4
Cheng, 2012 [36]	BCC	Dermoscopic	263 BCCs, 226 benign lesions (10-fold cross-validation)	ANN^c	AUROC: 0.846	4
Cheng, 2013 [37]	BCC	Dermoscopic	35 BCCs, 79 benign lesions (leave-one-out cross-validation)	ANN	AUROC: 0.902	4
Cheng, 2013 [40]	BCC	Dermoscopic	350 BCCs, 350 benign lesions (10-fold cross-validation)	ANN^c	AUROC: 0.981	2
Choudhury, 2015 [24]	BCC; CSCC	Dermoscopic; Non-dermoscopic	359 BCCs, CSCCs, MMs, and AKs (40 from each class randomly chosen for training; remainder used for testing)^b	MSVM^c	Accuracy: 94.6% (BCC), 92.9% (CSCC)	5
Chuang, 2011 [33]	BCC	Non-dermoscopic	84 BCCs, 235 benign lesions (3-fold cross-validation)	ANN	Accuracy: 95.0%; Sensitivity: 94.4%; Specificity: 95.2%	3
Dorj, 2018 [25]	BCC; CSCC	Non-dermoscopic	Training: 728 BCCs, 777 CSCCs, 768 MMs, and 712 AKs; Testing: 193 BCCs, 200 CSCCs, 190 MMs, and 185 AKs	ANN	Accuracy: 91.8% (BCC), 95.1% (CSCC); Sensitivity: 97.7% (BCC), 96.9% (CSCC); Specificity: 86.7% (BCC), 94.1% (CSCC)	5
Esteva, 2017 [16]	BCC; CSCC	Dermoscopic; Non-dermoscopic	Training: 127463 various lesions (9-fold cross-validation); Testing: 450 BCCs and CSCCs, 257 SKs	ANN	AUROC: 0.96	3
Ferris, 2015 [17]	BCC; CSCC	Dermoscopic	11 BCCs, 3 CSCCs, 39 MMs, 120 benign lesions (half used for training and half for testing)	Decision forest classifier	Sensitivity: 78.6%	2
Fujisawa, 2018 [18]	BCC; CSCC	Non-dermoscopic	Training: 974 BCCs, 840 CSCCs, 3053 various other lesions; Testing: 249 BCCs, 189 CSCCs, 704 various other lesions^b	ANN	Sensitivity: 80.3% (BCC), 82.5% (CSCC)	3
Guvenc, 2013 [47]	BCC	Dermoscopic	68 BCCs, 131 benign lesions (no cross-validation)	Logistic regression	Accuracy: 96.5%; AUROC: 0.988	4
Han, 2018 [23]	BCC; CSCC	Non-dermoscopic	Training: 19398 various lesions; Testing: 499 BCCs, 211 CSCCs, 2018 various other lesions^b,e	ANN	AUROC: 0.96 (BCC), 0.91 (CSCC); Sensitivity: 88.8% (BCC), 90.2% (CSCC); Specificity: 91.7% (BCC); 80.0% (CSCC)	3
Immagulate, 2015 [31]	BCC; CSCC	Non-dermoscopic	100 BCCs, 100 CSCCs, 100 AKs, 100 SKs, 100 nevi (10-fold cross-validation)	MSVM^c	Accuracy: 93%	5
Kefel, 2012 [49]	BCC	Dermoscopic	49 BCCs, 153 benign lesions (leave-one-out cross-validation)	ANN	AUROC: 0.925	4
Kefel, 2016 [38]	BCC	Dermoscopic	Training: 100 BCCs, 254 benign lesions; Testing: 304 BCCs, 720 benign lesions	Logistic regression	AUROC: 0.878	2
Kharazmi, 2011 [48]	BCC	Dermoscopic	299 BCCs, 360 benign lesions (no cross-validation)	Random forest classifier	AUROC: 0.903	4
Kharazmi, 2016 [50]	BCC	Dermoscopic	299 BCCs, 360 benign lesions (no cross-validation)	Random forest classifier	AUROC: 0.965	4
Kharazmi, 2017 [51]	BCC	Dermoscopic	Training: 149 BCCs, 300 benign lesions; Testing: 150 BCCs, 300 benign lesions	ANN	AUROC: 0.911; Sensitivity: 85.3%; Specificity: 94.0%	3
Kharazmi, 2018 [52]	BCC	Dermoscopic	295 BCCs; 369 benign lesions (10-fold cross-validation)	Random forest classifier	AUROC: 0.832; Sensitivity: 74.9%; Specificity: 77.8%	3
Lee 2018 [29]	BCC	Non-dermoscopic	Training: 463 BCCs, 1914 various lesions; Testing: 51 BCCs, 950 various lesions^b	ANN	Sensitivity: 91%	3
Maurya, 2014 [19]	BCC; CSCC	Dermoscopic; Non-dermoscopic	84 BCCs, 101 CSCCs, 77 MMs, 101 AKs (75 from each class used for training; remainder used for testing)^b	MSVM	Accuracy: 83.3% (BCC), 84.1% (CSCC)	5
Mishra, 2017 [39]	BCC	Dermoscopic	305 BCCs, 718 benign lesions (leave-one-out cross-validation)	Logistic regression	Accuracy: 72%^f	3
Møllersen, 2015 [30]	BCC; CSCC	Dermoscopic	Training: 37 MMs, 169 various lesions^g; Testing: 71 BCCs, 7 CSCCs, 799 various lesions^b	Hybrid model of linear and quadratic classifiers^c	Sensitivity: 100%; Specificity: 12%	2
Shakya, 2012 [41]	CSCC	Dermoscopic	53 CSCCs, 53 SKs (no cross-validation)	Logistic regression	AUROC: 0.991	4
Shimizu, 2014 [20]	BCC	Dermoscopic	69 BCCs, 105 MMs, 790 benign lesions (10-fold cross-validation)^b	Layered model of linear classifiers^c	Sensitivity: 82.6%	3
Shoieb, 2016 [26]	BCC	Non-dermoscopic	Training: 84 NMSC, 119 MMs; Testing: 64 BCC, 72 MM, 74 eczema, 31 impetigo	MSVM	Accuracy: 96.2%; Specificity: 96.0%; Sensitivity: 88.9%	5
Stoecker, 2009 [35]	BCC	Dermoscopic	42 BCCs, 168 various lesions(leave-one-out cross-validation)^b	ANN	AUROC: 0.951	2
Sumithra, 2015 [21]	CSCC	Non-dermoscopic	31 CSCCs, 31 MMs, 33 SKs, 26 bullae, 20 shingles (70% used for training; remainder used for testing)^b	Hybrid model of MSVM and k-NN classifiers^c	F-measure: 0.581	5
Upadhyay, 2018 [27]	BCC; CSCC	Non-Dermoscopic	239 BCCs, 88 CSCCs, 973 various lesions (24 from each class used for training; remainder used for testing)^b	ANN	Accuracy: 96.6% (BCC), 81.2% (CSCC); Sensitivity: 96.8% (BCC), 80.5% (CSCC)	3
Wahab, 2003 [32]	BCC	Non-Dermoscopic	54 BCCs, 54 DLE, 54 AV (34 from each class used for training; remainder used for testing)	ANN	Sensitivity: 90%	5
Wahba, 2017 [22]	BCC	Dermoscopic	29 BCCs, 27 nevi (46 total used for training and 10 for testing)	MSVM	Accuracy: 100%; Sensitivity: 100%; Specificity: 100%	5
Wahba, 2018 [42]	BCC	Dermoscopic	300 BCCs, 300 MMs, 300 nevi, 300 SKs (fivefold cross-validation)^b	MSVM	AUROC: Sensitivity: 100%; Specificity: 100%	3
Yap, 2018 [28]	BCC	Dermoscopic; Non-Dermoscopic	647 BCCs, 2270 various lesions (fivefold cross-validation)^b	ANN	Accuracy: 91.8%; Sensitivity: 90.6%; Specificity: 92.3%	3
Zhang, 2017 [44]	BCC	Dermoscopic	132 BCCs, 132 nevi, 132 SKs, 132 psoriasis (80% used for training; remainder used for testing)	ANN	Accuracy: 92.4%^d; Sensitivity: 85%^d; Specificity: 94.8%^d	3
Zhang, 2018 [45]	BCC	Dermoscopic	132 BCCs, 132 nevi, 132 SKs, 132 psoriasis (10-fold cross-validation)	ANN	Accuracy: 94.3%^d; Sensitivity: 88.2%^d; Specificity: 96.1%^d	3
Zhou, 2017 [46]	BCC	Dermoscopic	Training: 154 BCCs, 10,262 benign lesions; Testing: 50 BCCs, 1100 benign lesions	ANN	Accuracy: 96.8%^d; Sensitivity: 38%; Specificity: 99.5%^d	3

Abbreviations, AK Actinic keratosis, ANN Artificial neural network, AUROC Area under receiver operating characteristic, BCC Basal cell carcinoma, CSCC Cutaneous squamous cell carcinoma, k-NN k-nearest neighbors, MM Malignant melanoma, NMSC Non-melanoma skin cancer, MSVM Multiclass support vector machine, SK Seborrheic keratosis
^aQuality rating modified from Simel and Rennie [12]
^bCompetitive set included both benign and malignant lesions
^cStudy tested multiple classifiers; only the classifier that achieved the highest performance has been reported
^dFigures are derived from confusion matrix values and represent pooled BCC and CSCC classification in studies that tested both
^eTotal test set was derived from three different datasets (“Asan,” “Hallym,” “Edinburgh”), one of which was chronologically assorted and partitioned such that the oldest 90% of images were used for training and the newest 10% for testing [23]. However, number of lesions was provided only for the unpartitioned Asan dataset. Thus, we have estimated the total number of test lesions as 10% of the individual lesion classes in the unpartitioned Asan dataset plus all lesions in the Hallym and Edinburgh datasets
^fFigure represents approximation from histogram
^gTraining set represents figures provided in a previous study by the experimenters [58]. The classifier has not been retrained [20]

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