. 2021 Jul 31;11(8):1390. doi: 10.3390/diagnostics11081390

Table 2.

A comparison between state-of-the-arts methods with novel methods.

Reference	Simple	Dataset			Colored Images	Enhancement	Segmentation	Contribution	Contribution Achieved	Methods and Tools	No. of Classes	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Dice (%)
Reference	Simple	Number	Type	Free	Colored Images	Enhancement	Segmentation	Contribution	Contribution Achieved	Methods and Tools	No. of Classes	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision (%)	Dice (%)
[100]	Y	1	D	Y	N	Y	Y	Mobile Assessment	N	ABCD, MLP	2	88	66	93	N/A	N/A
[101]	N	1	A	N	N	Y	Y	Classify lesion Atlas	Y	Gabor filter, ABCD	2	94	91.25	95.83	N/A	N/A
[102]	N	1	A	N	Y	Y	Y	Classify lesion Atlas based on the thickness	Y	logistic regression with ANN	2	64.4	55.2	N/A	N/A	N/A
[103]	N	1	A	N	Y	Y	Y	Increase classification using a novel segmentation method	Y	Median Filter	2	N/A	N/A	N/A	N/A	N/A
[103]	N	1	D	Y	Y	Y	Y	Increase classification using a novel segmentation method	Y	Median Filter	2	N/A	N/A	N/A	N/A	N/A
[104]	N	3	A	N	Y	Y	Y	Segment and classify pigmented lesions	Y	Anisotropic Diffusion, Chan–Vese’s, ABCD, SVM	2	79.01	N/A	N/A	80	N/A
[104]	N	1	D	Y	Y	Y	Y	Segment and classify pigmented lesions	Y	Anisotropic Diffusion, Chan–Vese’s, ABCD, SVM	2	79.01	N/A	N/A	80	N/A
[105]	N	1	RS	N	-	-	-	classification	Y	Paraconsistent analysis network	3	93.79	N/A	N/A	N/A	N/A
[106]	Y	1	D	Y	N	Y	Y	Segmentation	Y	Delaunay Triangulation	2	75.91	67.46	95.93	N/A	N/A
[107]	N	1	H	N	Y	N	Y	Segmentation and Classification	Y	Granular Layer, Intensity Profiles	2	92.1	N/A	97.6	88.1	N/A
[108]	Y	1	F	N	-	-	-	Comparison of four Classification methods	Y	KNN with Sequential Scanning selection, KNN with GA, ANN with GA, Adaptive Neuro-Fuzzy Inference	2	94	N/A	N/A	N/A	N/A
[109]	Y	1	H	N	Y	N	Y	Classification lesions with and without segmentation	Y	Z-transforms	2	85.18	91.66	80	78.57	N/A
[110]	Y	1	JPGE	N	Y	N	N	Classification of Psoriasis	Y	PCA, SVM	2	100	100	100	N/A	N/A
[111]	Y	3	A	N	N	N	Y	Segmentation	Y	k-means	2	N/A	N/A	N/A	N/A	N/A
[112]	Y	1	A	N	Y	N	Y	Classification of skin cancer based on the deep of lesion using 3D reconstruction	Y	adaptive snake, stereo vision, structure from motion, depth from focus	8	86	98	99	N/A	N/A
[112]	Y	1	D	Y	Y	N	Y		Y		3	86	98	99	N/A	N/A
[113]	N	2	D	N	N	N	Y	Classification of melanoma	Y	a self-generated neural network, fuzzy neural networks with backpropagation (BP) neural networks	2	94.17	95	93.75	N/A	N/A
[114]	N	1	JPGE	N	Y	Y	Y	Segmentation and Classification	Y	fixed grid wavelet network, D-optimality orthogonal matching pursuit	2	91.82	92.61	91	N/A	N/A
[115]	N	1	SI	N	N	N	Y	Classification of the regular and irregular boundary of skin lesion	Y	1D time series for Lyapunov exponent and Kolmogorov–Sinai entropy	2	95	100	92.5	86.5	N/A
[115]	N	1	D	Y	N	N	Y		Y		2	95	100	92.5	86.5	N/A
[116]	Y	2	D	Y	Y	N	Y	Segmentation and classification of lesions to melanoma and Benign	Y	An ant colony, KNN, ANN	2	N/A	N/A	N/A	N/A	N/A
[117]	N	1	A	N	Y	N	Y	Segmentation and combine features, classification	Y	ABCD, SVM	2	90	72.5	94.4	N/A	N/A
[117]	N	2	D	Y	Y	N	Y	Segmentation and combine features, classification	Y	ABCD, SVM	2	90	72.5	94.4	N/A	N/A
[118]	N	2	D	N	N	Y	Y	Codebook generated from a bag of features	Y	Histogram of Gradients, Histogram of Lines, Zernike moments	2	92.96	96.04	84.78	N/A	N/A
[119]	N	1	D	Y	Y	N	Y	Classification of skin lesion using feature subset selection	Y	Optimum-path forest integrated with a majority voting	2	94.3	91.8	96.7	N/A	N/A
[120]	N	1	SIA scope	N	N	N	Y	Analysis of multispectral skin lesion	Y	Box-counting dimension and lacunarity, Hunter score pattern detection, RBF kernel, SVM	2	97	59.6	97.8	N/A	N/A
[121]	N	2	D	Y	Y	Y	Y	Automatic segmentation of skin lesion	Y	The grab-Cut algorithm, k-means	2	96.04	89	98.79	N/A	91.39
[122]	Y	1	CCD	N	Y	N	Y	Classification of skin lesion using a smartphone	Y	Otsu’s, Minimum Spanning Tree, Color Triangle	2	87.98	89.09	86.87	N/A	N/A
[123]	Y	1	D	Y	Y	Y	N	Enhancement and fusion of skin lesion features for classification	Y	Wavelet transform, Curvelet transform, local binary pattern, SVM	2	86.17	78.93	93.25	N/A	N/A
[124]	N	-	-	-	N	Y	Y	Extract fabric characteristics for lesion classification	Y	Gabor filters based on shark smell optimizing method, K-means	-	N/A	N/A	N/A	N/A	N/A
[125]	N	3	D	Y	N	Y	Y	Skin lesion segmentation and recognition based on fused features	Y	ABCD, fuzzy C-means, pair threshold binary decomposition, HOG, linear discriminant analysis, linear regression, complex tree, W-KNN, Naive Bayes, ensemble boosted tree, ensemble subspace discriminant analysis	2, 3	99	98.5	99	98.5	N/A
[126]	N	1	D	Y	Y	Y	Y	Skin lesion segmentation with several classifiers	Y	2-D Gabor wavelet, OTSU’s, median filtering, morphological operations, m- mediod classifier, SVM, Gaussian Mixture Modeling	2	97.26	96.48	96.9	N/A	N/A
[127]	N	3	D	Y	Y	Y	Y	Classify the selected features of parallel fusion of features after segmentation	Y	Global maxima and minima, uniform and normal distribution, mean, mean deviation, HOG, Harlick method, M-SVM	2	93.2	93	97	93.5	N/A
[128]	N	3	D	Y/N	N	Y	Y	Optimize features of skin lesion and classify these features	Y	Particle Swarm Optimization, KNN, SVM, decision tree	2	N/A	N/A	N/A	N/A	N/A
[129]	N	1	D	Y	Y	Y	Y	Using high discriminative features for melanoma classification	Y	Histogram correction, OTSU’s, corner detection, Gray-level co-occurrence matrix features, Daugman’s rubber sheet model, RUSBoost, linear SVM	2	95	95	95	N/A	92
[130]	N	3	D	Y	N	Y	Y	Classify melanoma based on the main frequencies from dermoscopic images	Y	ABCD, HU Moments, GLCM, Structural Co-occurrence matrix, MLP, LSSVM, Minimal Learning Machine	2	89.93	92.15	89.9	N/A	91.05
[131]	N	1	I	N	-	Y	Y	Differentiate infrared spectroscopy of skin lesion	Y	Fourier transform infrared, Morphological information, PCA	2	85	N/A	N/A	N/A	N/A
[132]	N	1	D	Y	Y	Y	Y	Classifying four skin lesions based on the Gray-level difference method	N	Gray-level difference method, ABCD, SVM	4	100	100	100	N/A	N/A
[133]	N	1	CCD	N		Y	Y	Segment skin lesion and using a hybrid classifier to classify these lesions	Y	ABCD, pigment distribution and texture, GLCM, Log-linearized Gaussian mixture neural network, KNN, linear discriminant analysis, LDA, SVM, majority vote	3	98.5	95	99.5	N/A	N/A
[134]	N	1	D	Y	Y	Y	Y	Classify typical and atypical pigment network to diagnose melanoma	Y	Laplacian filter, median filter, polynomial curve fitting, connected component analysis, 2D Gabor filters, Gray-Level co-occurrence Matrix, Pearson product-moment co-relation, Probabilistic SVM, ANN	2	86.7	84.6	88.7	N/A	N/A
[135]	N	1	LIBS spectra	N	-	Y	Y	Classification of skin tissue	Y	laser-induced breakdown spectroscopy, PCA, KNN, SVM	2	76.84	74.2	86.9	N/A	N/A
[136]	Y	2	A	N	N	Y	Y	Classification of melanoma versus nevi by correlation bias reduction	Y	2D wavelet packet decomposition, SVM recursive feature elimination (SVM -RFE)	2	98.28	97.63	100	N/A	N/A
[136]	Y	2	D	Y	N	Y	Y		Y		2	98.28	97.63	100	N/A	N/A
[137]	Y	1	D	N	Y	Y	Y	Classification of lesions into melanoma and nevi	Y	Gaussian Filter, KNN, SVM	2	96	97	96	97	N/A
[138]	Y	1	A	N	Y	N	Y	Using of blue-whitish structure for melanoma classification	Y	multiple instance learning (MIL) paradigm, Markov network, SVM	2	84.5	74.42	87.9	61.54	N/A
[138]	Y	1	D	Y	Y	N	Y	Using of blue-whitish structure for melanoma classification	Y		2	84.5	74.42	87.9	61.54	N/A
[139]	N	1	A	N	Y	N	Y	Classify lesion into melanoma or nevi based of color features	Y	K-means, pixel-based classification	2	89.42	N/A	N/A	N/A	N/A
[140]	Y	1	D	Y		Y	Y	Segmentation and classification of skin lesions	Y	Hough Transform, ABCD, SP-SIFT, LF-SLIC region labeling	2	96	N/A	N/A	N/A	93.8
[141]	Y	2	D	Y	Y	Y	Y	Detect the boundaries of lesions to classify into melanoma and nevi	Y	Kullback-Leibler divergence, local binary patterns, SVM, KNN	2	80.7	N/A	N/A	N/A	N/A
[142]	N	1	JPGE	N	Y	Y	Y	A QuadTree-based melanoma detection system based on color	Y	hybrid thresholding method, adaptive histogram thresholding, Euclidean distance transform, QuadTree, Kolmogorov-Smirnov, SVM, ANN, LDA, random forests	2	73.8	75.7	73.3	N/A	N/A
[143]	Y	1	D	Y	N	Y	Y	Segmentation and classification of skin lesion	Y	Median Filter, watershed segmentation, ABCD, GLCM, KNN, RF, SVM	2	89.43	91.15	87.71	N/A	N/A
[144]	Y	1	D	Y	N	Y	Y	Segmentations of Enhanced dermoscopic lesion images	Y	wavelet transform, morphological operations, Gray Thresholding, Cohen–Daubechies–Feauveau biorthogonal wavelet, Active contour, Color enhancement, Adaptive thresholding, Gradient vector flow	2	93.87	N/A	N/A	N/A	92.72
[145]	Y	1	D	Y	N	Y	Y	Three distinct features to classify melanoma	N	ABCD, Otsu, Chan–Vese, Dull-Razor, ANN	2	98.2	98	98.2	N/A	N/A
[146]	N	2	A	N	N	Y	Y	Classification three lesions based on shape, fractal dimension, texture, and color features	Y	recursive feature elimination, GLCM, fractal-based regional texture analysis, SVM, RBF	3	98.99	98.28	98.48	N/A	91.42
[146]	N	2	D	Y	N	Y	Y		Y		3	98.99	98.28	98.48	N/A	91.42
[147]	N	2	A	N	Y	N	N	Extraction of features using frequency domain analysis and classify these features	Y	Cross spectrum-based feature extraction, Spatial feature extraction, SVM-RFE with CBR	4	98.72	98.89	98.83	N/A	N/A
[147]	N	2	D	Y	Y	N	N		Y		4	98.72	98.89	98.83	N/A	N/A
[148]	Y	1	D	N	Y	N	N	Improve bag of dense features to Classify skin lesions	Y	Gradient Location, Orientation Histogram, color features, SVM	2	78	N/A	N/A	N/A	N/A
[149]	N	2	D	Y	N	Y	Y	CAD system for clinical assist	N	chroma based deformable models, speed function, Chan-Vese, Wilcoxon Rank Sum statistics, Discrete Wavelet Transform, Asymmetry, and Compactness Index, SVM	2	88	95	82	N/A	N/A
[150]	Y	2	D	Y	N	Y	Y	Segmentation of skin lesions using fuzzy pixels classification and histogram thresholding	Y	fuzzy classification, histogram thresholding	2	88.4	86.9	92.3	N/A	76
[151]	N	1	D	Y	Y	Y	Y	Lesion classification based on feature similarity measurement for codebook learning in the bag-of-features model	Y	Codebook learning, k-means, color histogram, scale-invariant feature transform (SIFT)	2	82	80	83	N/A	N/A
[151]	N	1	C	Y	Y	Y	Y		Y		2	82	80	83	N/A	N/A
[152]	Y	1	D	Y	Y	Y	Y	Segmentation and classification of skin lesions using Kernel sparse representation	Y	Sparse coding, kernel dictionary, K-SVD	3	91.34	93.17	91.48	N/A	91.25
[153]	N	1	D	N	N	Y	Y	Improve skin lesion classification using borderline characteristics	Y	Gradient-based Histogram Thresholding, Local Binary Patterns Clustering, Euclidean distance, Discrete Fourier Transform spectrum (DCT), power spectral density (PSD), SVM, Feedforward Neural Network (FNN)	2	91	68	96	N/A	N/A
[153]	N	1	C	Y	N	Y	Y		Y		2	91	68	96	N/A	N/A
[154]	Y	1	D	N	Y	N	Y	Multi-resolution-Tract CNN	N	AlexNet, GPU	10	79.5	N/A	N/A	N/A	N/A
[155]	Y	1	D	Y	Y	Y	Y	Automatic segmentation and classification for skin lesions	Y	CNN with 50 layers, residual learning, SoftMax, SVM, Augmentation, GPU	2	94	N/A	N/A	N/A	N/A
[156]	N	1	D	Y	N	Y	Y	Classification of segmented skin lesions	Y	U-Net, Sparse coding, Deep Residual Network (DRN), Augmentation	2	76	82	62	N/A	N/A
[157]	Y	1	D	Y	N	Y	Y	Segmentation of skin lesions using deep learning	Y	fully convolutional networks (FCN), VGG, Augmentation	2	N/A	N/A	N/A	N/A	89.2
[158]	Y	1	D	Y	Y	Y	Y	Automatic Skin Lesion Segmentation Using CNN	Y	FCN with 19 layers, Jaccard Distance, Augmentation, GPU	2	95.5	91.8	96.6	N/A	92.2
[159]	Y	3	D	Y	Y	Y	N	Detection of melanoma using CNN and regularized fisher framework	Y	ResNet50, transfer learning, SoftMax, SVM, Augmentation, GPU	2	78.3	35	88.8	N/A	N/A
[159]	Y	1	C	Y	Y	Y	N		Y		2	78.3	35	88.8	N/A	N/A
[160]	N	1	D	Y	N	Y	Y	Evaluation of skin lesion using Levenberg neural networks and stacked autoencoders clustering	Y	ABCD, morphological analysis, Levenberg–Marquardt neural network	2	N/A	98	98	N/A	N/A
[161]	N	1	D	Y	Y	N	N	Classify limited and imbalanced skin lesion images	N	Adversarial Autoencoder with 19 layers, Augmentation, GPU	2	N/A	N/A	83	N/A	N/A
[162]	Y	1	D	Y	Y	N	N	Ensemble different CNN for skin lesion classification	Y	GoogLeNet, AlexNet, ResNet, VGGNet, Sum of the probabilities, Product of the possibilities, Simple majority voting (SMV), Sum of the maximal probabilities (SMP), Weighted ensemble of CNN, Augmentation, GPU	3	86.6	55.6	78.5	N/A	N/A
[163]	N	1	D	Y	Y	N	N	Skin lesion analysis using Multichannel ResNet	Y	Ensemble multi-ResNet50, ANN, concatenated Fully Connected Layer, Augmentation, GPU	3	82.4	N/A	N/A	N/A	N/A
[164]	N	1	A	N	Y	Y	Y	Classify skin lesions based on border thickness	Y	GoogleNet, Breslow index	5	66.2	89.19	85	N/A	N/A
[165]	N	1	D	Y	Y	Y	Y	Classify lesion using fused features that extracted from deep learning and image processing	Y	ResNet50, Telangiectasia Vessel Detection Algorithm, transfer learning, GPU	5	N/A	N/A	N/A	N/A	N/A
[166]	Y	1	D	Y	Y	N	N	Classify skin lesions over IoT using deep learning	Y	CNN with nine layers, IoT,	7	81.4	N/A	N/A	N/A	N/A
[168]	Y	3	D	Y	Y	Y	N	Skin lesion classification using depthwise separable residual CNN	Y	Non-local means filter, contrast-limited adaptive histogram equalization, discrete Wavelet transforms, depthwise separable residual DCNN	2	99.5	99.31	100	N/A	N/A
[168]	Y	1	C	Y	Y	Y	N		Y		2	99.5	99.31	100	N/A	N/A
[169]	Y	1	D	Y	Y	Y	Y	Classify skin lesion using Attention Residual Learning	Y	Attention residual learning CNN, ResNet50, transfer learning, Augmentation, GPU	3	N/A	N/A	N/A	N/A	N/A
[170]	Y	1	D	Y	Y	N	N	Classify skin lesion using CNN with novel regularization method	Y	CNN 7 layers, standard deviation of the weight matrix, GPU	2	97.49	94.3	93.6	N/A	N/A
[171]	N	1	D	Y	N	Y	Y	Classify skin lesion by Incorporating the knowledge of dermatologists to CNN	Y	ResNet50, DermaKNet, Modulation Block, asymmetry block, AVG layer, Polar AVG layer.	3	91.7	N/A	65.2	N/A	N/A
[172]	Y	1	-	-	Y	Y	Y	Classify skin lesions based on the novel 7-point melanoma checklist using Multitask CNN	Y	Multitask CNN, 7-point melanoma checklist, Augmentation, GPU	3	87.1	77.3	89.4	63	N/A
[173]	Y	1	D	Y	Y	N	N	Classify skin lesion using Aggregated CNN	Y	ResNet50, ResNet101, fisher vector (FV), SVM, Chi-squared kernel, transfer learning, Augmentation, GPU	2	86.81	N/A	N/A	N/A	N/A
[174]	Y	-	-	-	Y	N	N	Using CNN as a feature extractor for skin lesion images and classify these features	Y	Alex-Net, ECOC SVM, transfer learning	4	94.2	97.83	90.74	N/A	N/A
[175]	Y	1	D	Y	Y	N	N	Classification of skin neoplasms using CNN and transfer learning with web and mobile application	N	Inception V3 (GoogleNet), transfer learning, Augmentation, GPU	-	91	N/A	N/A	N/A	N/A
[176]	N	1	C	N	Y	N	Y	An application used through the cloud to classify diseases of face skin	Y	LeNet-5, AlexNet and VGG16, transfer learning, Augmentation, GPU	5	N/A	N/A	N/A	N/A	N/A
[177]	Y	2	D	Y	Y	Y	Y	Skin lesion classification using 4 CNNs and ensembling of the final classification results	Y	AlexNet, VGG, ResNet-18, ResNet-101, SVM, MLP, random forest, transfer learning, Augmentation, GPU	3	87.7	85	73.29	N/A	N/A
[178]	Y	1	D	Y	Y	N	N	Compare the ability of deep learning model to classify skin lesions with expert dermatologists	Y	ResNet50, local outlier factor, transfer learning, GPU	2	N/A	87.5	60	N/A	N/A
[179]	N	1	D	N	Y	Y	Y	Evolving the deep learning model		PSO, hybrid learning PSO, Firefly Algorithm, spiral research action, probability distributions, crossover, mutation, K-Means, VGG16, Augmentation, GPU	2	73.76	N/A	N/A	N/A	N/A
[179]	N	2	D	Y	Y	Y	Y	Evolving the deep learning model			2	73.76	N/A	N/A	N/A	N/A
[181]	Y	3	D	Y	Y	Y	Y	Combine and expand current segmentation CNN to enhance the classification of skin lesions	Y	U-Net, ResNet34, LinkNet34, LinkNet152, fine-tuning, PyTorch, transfer learning, Augmentation, GPU, Jaccard-loss,	2	N/A	N/A	N/A	N/A	N/A
[182]	N	1	D	Y	N	Y	Y	skin lesion segmentation based using geodesic morphological active contour	Y	Gaussian filter, Otsu’s threshold, deformable models, partial differential equation, Mathematical morphology, active geodesic contour, neural network, deep learning, statistical region merging (SRM),	2	94.59	91.72	97.99	N/A	89
[183]	Y	1	-	-	Y	N	N	Erythema migrans and the other confounding lesions of skin using	Y	ResNet50, Keras, TensorFlow, fine-tuning, transfer learning, Augmentation, GPU	4	86.53	76.4	75.96	N/A	92.09
[184]	Y	1	D	Y	Y	N	N	Comparing between the CNN and 112 dermatologists for skin lesion detection	Y	ResNet50, fine-tuning, transfer learning, Augmentation, GPU	5	N/A	56.5	98.2	N/A	N/A
[185]	Y	2	D	Y	Y	Y	Y	Segmentation of skin lesions by ensemble the segmentation output of 2 CNN	Y	DEEPLABV3+, Mask R-CNN, ABCD, fine-tuning, transfer learning, Augmentation, GPU	2	94.08	89.93	95	N/A	N/A
[186]	Y	1	C	Y	N	Y	Y	Proposing an algorithm that able to train CNN with limited data	Y	Inception V3 (GoogleNet), PECK, SCIDOG, SVM, RF, fine-tuning, transfer learning, Augmentation, GPU	2	91	92	93	N/A	90.7
[187]	Y	1	C	N	Y	N	N	Classify skin disease of faces using Euclidean space to compute L-2 distance between images	Y	ResNet152, InceptionResNet-V2, fine-tuning, Euclidean space, L-2 distance, transfer learning, Augmentation, GPU	4	87.42	97.04	97.23	N/A	N/A
[188]	Y	1	D	Y	Y	Y	Y	Neural Architecture Search to increase the size of the network based on the dataset size	Y	VGG8, VGG11, VGG16, 5-fold validation, Neural Architecture Search (NAS), hill-climbing, transfer learning, Augmentation, GPU	2	77	N/A	N/A	N/A	N/A
[189]	Y	2	D	Y	Y	Y	Y	Skin lesion segmentation and pixel-wise classification using encoder and decoder network	Y	CNN, encoder-decoder deep network with skip connection, softmax, transfer learning, Augmentation, GPU	2	95	97	96	N/A	N/A
[190]	Y	2	D	Y	Y	Y	Y	Multitasks DCNN for skin lesion segmentation, detection, and classification	Y	Multitask DCNN, Jaccard distance, focal loss, Augmentation, GPU	2	95.9	83.1	98.6	N/A	95
[191]	Y	1	D	Y	Y	Y	Y	Light Lightweight CNN for skin lesion segmentation and classification	Y	Lightweight CNN, MobileNet, DenseNet, U-Net, focal loss, fine-tune, transfer learning, Augmentation, GPU	2	96.2	93.4	97.4	N/A	88.9
[192]	N	1	D	Y	Y	Y	N	Enhancement and classification of dermoscopic skin images	Y	StyleGANs, 43 CNNs (ResNet50, VGG11, VGG13, AlexNet, SENet, etc.) max voting, fine-tune, transfer learning, Augmentation, GPU	8	99.5	98.3	99.6	N/A	92.3
[193]	Y	1	D	Y	Y	Y	Y	Skin lesion classification based on CNN	Y	Deep-class CNN, Augmentation, GPU	2	75	73	78	N/A	N/A
[194]	Y	2	D	Y	Y	Y	Y	Skin lesion segmentation using encoder-decoder FCN	Y	FCN, GPU	3	96.92	96.88	95.31	N/A	N/A
[195]	Y	3	D	Y	Y	N	Y	Classify skin melanoma by extracting ROI and CNNS	Y	AlexNet, ResNet101, GoogleNet, Multiclass SVM, SoftMax, Histogram based windowing process, hierarchical clustering, fine-tune, transfer learning, Augmentation, GPU	3	98.14	97.27	98.60	N/A	88.64
[195]	Y	1	C	Y	Y	N	Y	Classify skin melanoma by extracting ROI and CNNS	Y		3	98.14	97.27	98.60	N/A	88.64
[196]	Y	3	D	Y	N	Y	Y	The fusion of extracted deep features of a skin lesion for classification	Y	Biorthogonal 2-D wavelet transform, Otsu algorithm, Alex and VGG-16, PCA, fusion, fine-tune, transfer learning, Augmentation, GPU	2	99.9	99.5	99.6	N/A	N/A
[197]	N	3	D	Y	Y	Y	Y	Ensemble multiscale and multi-CNN network	Y	EfficientNetB0, EfficientNetB1, SeResNeXt-50, fusion, fine-tune, transfer learning, Augmentation, GPU	7	96.3	N/A	N/A	91.3	82
[198]	Y	2	D	Y	Y	Y	Y	Classification of skin lesions by multiclass multilevel using traditional machine learning and transfer learning	Y	K-means, Otsu’s thresholding, GLCM, ANN. k-fold validation, AlexNet, fine-tune, transfer learning, Augmentation, GPU	4	93.02	87.87	98.17	97.96	N/A
[199]	N	2	D	Y	N	Y	Y	Optimized algorithm for weight selection to minimize the output of the network	Y	The bubble-net mechanism, Whale optimization algorithm (WOA), Lévy Flight Mechanism, genetic algorithm, shark smell optimization (SSO), world cup optimization algorithm, grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), LeNet, fine-tune, transfer learning, GPU	2	N/A	N/A	N/A	N/A	N/A
[200]	Y	3	D	Y	N	Y	Y	semantic skin lesion segmentation with parameters reducing	Y	U-Net, FCN8s, DSNet, Augmentation, GPU	7	N/A	87.5	95.5	N/A	N/A
[201]	N	3	D	Y	Y	Y	Y	Different CNN network integration for segmentation and multiple classification stage	Y	Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201	7	89.28	81	87.16	N/A	81.82
[202]	Y	3	D	Y	Y	Y	Y	Skin lesion segmentation based on CNN	Y	CIElab, FCN, U-Net, Augmentation, GPU	2	N/A	N/A	N/A	N/A	87.1
[205]	Y	1	D	Y	Y	N	N	Classify the challenging dataset ISIC2018	Y	AlexNet, 10-fold cross-validation, fine-tune, transfer learning, Augmentation, GPU	7	92.99	70.44	96	62.78	N/A
[204]	Y	1	D	Y	Y	N	N	Classify the challenging dataset ISIC2019	Y	GoogleNet, Similarity score, bootstrap weighted SVM classifier, SoftMax, fine-tune, transfer learning, Augmentation, GPU	9	98.70	95.6	99.27	95.06	N/A