Table 10.

Performance comparison of different automated melanoma detection methods. The performance metrics for each classifier are the area under the curve (AUC) the accuracy (AC), specificity (SP) and sensitivity (SN)

Author	Year	Dataset	Features	Classifier	AUC	AC	SP	SN.
Majtner et al. [59]	2016	ISIC2016	RSurf, LBP, CNN-based	SVM	0.78	$82.60\%$	$89.80\%$	$53.30\%$
Gutman et al. [42]	2016	ISIC2016	Deep features	Deep Networks	0.80	$85.50\%$	$94.10\%$	$50.70\%$
Oliveira et al. [74]	2017	ISIC2016	Texture, color, geometric features	Ensemble of classifiers	−	$94.30\%$	$96.70\%$	$91.80\%$
Lopez et al. [54]	2017	ISIC2016	Deep features	VGG16 CNN	−	$81.33\%$	$84.00\%$	$78.66\%$
González-Díaz [39]	2018	ISIC2017	Deep features	CNN	0.87	−	−	−
Menegola et al. [62]	2017	ISIC2017	Deep features	VGG16 network	0.91	$72.10\%$	$95.00\%$	$54.70\%$
Esteva et al. [30]	2017	Images from ISIC, the Edinburgh Dermofit Library and the Stanford Hospital	Deep features	CNN	0.94	$72.10\%$	−	−
Matsunaga et al.[61]	2017	ISIC2017 + 1444 ISIC images	Deep features	CNN Ensemble	0.92	−	−	−
Reboucas Filho et al.[78]	2018	ISIC2017	GLCM, LBP, Hu moments	SVM	0.89	$89.93\%$	$89.90\%$	$92.15\%$
		ISIC2016	GLCM, LBP, Hu moments	SVM	0.92	$94.50\%$	94.57	$95.23\%$
		PH2	GLCM, LBP, Hu moments	SVM	0.99	$99.00\%$	99.20	$99.40\%$
Mahbod et al. [81]	2019	ISIC2017	Deep features (AlexNet, VGG16, ResNet-18)	SVM	0.84	−	−	−
Brinker et al. [18]	2019	ISIC Dermoscopic Archive	Deep features	CNN	−	−	$77.90\%$	$82.30\%$
Proposed Approach	2020	ISIC2017+ 250ISIC melanoma images	Superpixel graph features	Random forests	0.99	$97.50\%$	$95.10\%$	$100.00\%$