1. Introduction

2. Artificial Intelligence in the Diagnosis of Melanoma

3. Dermoscopic Images

3.1. Distinguishing Melanoma from Benign Lesions

Masood et al. classified clinical and dermoscopic photographs as benign/melanoma using ANN and compared the performances of three different ANN algorithms (Levenberg-Marquardt (LM), resilient backpropagation (RP), scaled conjugate gradient (SCG)). SCG gave the most successful results with 92.6% sensitivity and 91.4% specificity. LM achieved a specificity of 95.1% in benign lesions, but it was not as successful as SCG in melanoma [35]. In [36], a fusion ML model consisting of five individual top-ranked algorithms from the ISBI 2016 Challenge was applied for melanoma detection, and its performance was compared to dermatologists. Their model achieved an AUROC of 0.86 and was more accurate than dermatologists; however, applying ML classifications to dermatologist evaluations increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8% [37].

Recently, there has been a surge in studies on the discrimination of melanocytic lesions (benign/malignant) using CNNs on dermoscopic photographs. Chanki Yu et al. used a pre-trained CNN model (VCG-16) in diagnosing acral melanoma, compared to both general practitioners and dermatologists. They performed 2-fold cross-validation and split the dataset into a 50/50 train-test split. The model achieved a similar AUROC value to experts and was significantly superior to the non-expert group [38]. Abbas et al. also designed a seven-layer deep CNN to discriminate between acral melanoma and benign nevus. They used 724 dermoscopic images from Chanki Yu et al’s [38] dataset and 4,344 dermoscopic images generated by data augmentation techniques. The authors also applied transfer learning to the AlexNet and ResNet-18 and fine-tuned them by modifying their last layers. An AUC of 0.97, 0.96, and 0.91 was obtained with ResNet-18, AlexNet, and the proposed ConvNet, respectively [39]. Another study proposed a CNN model to distinguish combined nevi from melanoma. Moleanalyzer Pro, previously trained on more than 120,000 dermoscopic images, was used in the study, and 72 dermoscopic images (36 combined nevus and 36 melanoma) were evaluated. When compared to 11 dermatologists divided into 3 groups (beginner/qualified/expert), the model outperformed all of them, revealing 97.1% sensitivity and 78.8% specificity [40].

Although the success of dermatologists is compared with AI, few dermatologists were included. To address this drawback, Brinker et al. compared the performance of a CNN algorithm (Resnet) trained only on open-source dermoscopic images with 157 dermatologists, resulting in seven dermatologists being more accurate than CNNs [41]. Furthermore, Giulini et al. combined CNN and human expertise in the diagnosis of melanoma. In the study, 64 physicians (33 dermatologists, 11 dermatology residents, and 20 general practitioners) assessed 100 dermoscopic photographs of 50 melanomas and 50 benign nevi. After a duration of 4 months, the same photographs were reevaluated in a different order with CNN assistance by the physicians. In the session with CNN assistance, the mean sensitivity and specificity increased to 67.88% and 73.72% from 56.31% and 69.28%, respectively [42].

Hybrid models are also commonly studied in the literature; Mahbod et al. used 3 pre-trained CNN models (AlexNet, VGG16, and ResNet-18) for feature extraction, followed by an SVM-based classification step. The final classification result was obtained by averaging the output of the individual models. The resulting ensemble model was evaluated on 150 validation images and achieved an AUC of 90.69%, surpassing the performance of the individual CNN models (AlexNet, VGG16, and ResNet-18) [43]. Ningrum et al. constructed a hybrid model by integrating dermoscopic pictures and patient data to diagnose melanoma. They employed a model that utilized both CNN to analyze photos and ANN to analyze patient data in order to categorize patients as melanoma or nonmelanoma. The results were compared with CNNs from only analyzing images. The CNN+ANN model achieved an accuracy of 92.34%, surpassing the accuracy of the CNN model alone at 73.69% [44].

While AI demonstrates success in studies, its application and implementation in real-world scenarios are crucial. Hekler et al. assessed the efficacy of DL in categorizing lesions by employing multiple real-world lesion images, single lesion images, and modified lesion images. The model displayed markedly enhanced performance when utilizing multiple real-world images, particularly in uncertainty estimation and robustness [45]. Specifically, the utilization of AI in melanoma screening is poised to substantially alleviate the workload on clinicians. To showcase AI's potential as a melanoma screening tool, Crawford et al. explored the feasibility of employing AI to identify potential melanomas in self-referred patients concerned about the malignancy of their skin lesions. The AI successfully identified 11 of 17 malignant lesions, achieving an accuracy of 73.56%, exceeding the accuracy of 4 out of 5 dermatologists involved in the study [46].

The lack of transparency of AI techniques reduces their reliability for users. To address this issue, Chanda et al. developed an explainable AI (XAI) algorithm. In the task of predicting melanoma, the algorithm explains the basis of its prediction. The investigation revealed that the XAI increased clinicians' diagnostic confidence while also enhancing their trust in the assistance provided by XAI [47]. Correira et al. have introduced a method that utilizes an interpretable prototypical-part model that integrates binary masks, automatically generated by a segmentation network and user-refined prototypes. This model is designed to incorporate non-expert feedback, ensuring that the learned prototypes specifically relate to important areas within the skin lesion while excluding irrelevant factors beyond its boundaries. By following these two distinct information pathways, the proposed approach demonstrates superior diagnostic performance when compared to non-interpretable models [48].

Table 2. Algorithms that distinguish between melanoma and benign lesions through dermoscopic images.

Table 2. Algorithms that distinguish between melanoma and benign lesions through dermoscopic images.

Publication	End-point	Dataset	Algorithm	Performance
Masood et al. [35]	Classification (benign/melanoma)	135 images (Clinical + dermoscopic) 107 for training, 14 for validation 14 for testing	Compared 3 ANN algorithms (RP, LM, SCG)	SCG: Acc: 91,9% Sen: 92.6% Spe: 91.4% LM: Acc: 91,1% Sen: 85.2% Spe: 95.1% RP: Acc: 88,1% Sen: 77.8% Spe: 95.1%
Aswin et al. [49]	Classification (Cancerous/Non-cancerous)	30 dermoscopic images for training 50 dermoscopic images for testing	Hybrid Genetic Algorithm + ANN	Acc: 88%
Xie et al. [50]	Classification (MM/BN)	Dermoscopic images Xanthous race:240 images (80 MM, 160 BN) Caucasian race: 360 images (120 MM, 240 BN)	Proposed: meta-ensemble model of multiple neural network ensembles Ensemble 1: single-hidden-layer BP nets with same structures Ensemble 2: single-hidden-layer BP nets and fuzzy nets Ensemble 3: double-hidden-layer BP nets with different structures	Xanthous race: Sen: 95% Spe: 93.75% Acc: 94.17% Caucasian race: Sen: 83.33% Spe: 95% Acc: 91.11%
Marchetti et al. [36]	Classification (MM/BN)	ISBI 2016 challenge dataset [51], MM:248 images BN:1031 images Train set:900 images Test set:379 images Reader study:100 images (50 MM, 50 BN)	Five methods (unlearned and machine learning) were used to combine individual automated predictions into “fusion” algorithms	Top Fusion Algorithm: Greedy Fusion: Sen: 58% Spe: 92% AUC: 86% Dermatologists: Sen: 82% Spe: 59% AUC: 71%
Marchetti et al. [37]	Classification (MM/BN/SK) and (biopsy/observation)	ISIC Archive [52]: 2,750 dermoscopy images (521 (19%) MM, 1,843 (67%) BN, and 386 (14%) SK) Training set: 2,000 images Validation: 150 images Test set: 600 images	ISBI 2017 Challenge top ranked algorithm	Algorithm: Sen: 76% Spe: 85% AUC: 0.87 Dermatologists: Sen: 76.0% Spe: 72.6% AUC: 0.74
Cueva et al. [53]	Classification (Cancerous/Non-cancerous)	PH² database [54] Training set: 30 images (10 MM, 10 common mole, 10 no-common mole) Test set: 201 images (80 common mole, 80 no-common mole, 41 MM)	ANN with backpropagation algorithm	After an analysis of 201 images in the algorithm developed a performance of 97.51% was obtained
Navarro et al. [55]	Segmentation and registration to evaluate lesion change	ISIC archive [52]: Training set: 2000 dermoscopic images Validation: 150 dermoscopic images Test set: 600 dermoscopic images	Segmentation: LF-SLIC Registration:SP-SIFT	Acc: 0.96 for segmentation
Yu C. et al. [38]	Classification (melanoma/non-melanoma)	725 images (AM: 350 images, BN: 374 images) Group A: 175 images AM, 187 images BN Group B: 175 images AM, 187 images BN Training set: Group A images for training Group B Group B images for training Group A Test set: Group A images for Group A Group B images for Group B	CNN (VCG-16)	Group A: CNN: Sen: 92.57 Spe: 75.39 Acc: 83.51 Expert: Sen: 94.88 Spe: 68.72 Acc: 81.08 Non-expert: Sen: 41.71 Spe: 91.28 Acc: 67.84 Group B: CNN: Sen: 92.57 Spe: 68.16 Acc: 80.23 Expert: Sen: 98.29 Spe: 65.36 Acc: 81.64 Non-expert: Sen: 48.00 Spe: 77.10 Acc: 62.71
Abbas et al. [39]	Classification (benign nevus/acral melanoma)	724 images from Yonsei University [38] (350 acral melanoma, 374 benign nevi) 4344 images with data augmentation (2100 acral melanoma, 2244 benign nevi)	Compared three CNN algorithms (Seven-layered deep CNN, ResNet-18, AlexNet)	ResNet-18 Acc: 0.97 AUC: 0.97 AlexNet: Acc: 0.96 AUC: 0.96 Proposed ConvNet Acc: 0.91 AUC: 0.91
Fink et al. [40]	Classification (Benign/Malignant)	Training set: >120.000 dermoscopic images and labels Test set: 72 images (36 combined naevi, 36 melanomas)	CNN (Moleanalyzer-Pro) based on a GoogleNet Inception_v4 architecture	CNN: Sen: 97.1% Spe: 78.8% Dermatologists: Sen: 90.6% Spe: 71.0 %
Phillips et al. [56]	Classification (MM/dysplastic nevi/other)	Pretrained algorithm Training set (in study): 289 images (36 melanoma lesions; 67 nonmelanoma lesions, 186 control lesions) Test set:1550 images	SkinAnalytics (CNN)	The algorithm: İphone 6s image: AUC: 95,8% Spe: 78,1% Galaxy S6 image: AUC: 93,8% Spe: 75,6% DSLR image: AUC: 91,8% Spe: 45,5% Specialists: AUC: 77,8% Spe: 69,9%
Martin-Gonzalez et al. [57]	Classification (benign/ malignant skin lesion)	Pretrained with 37,688 images from ISIC Archive [52] 2019 and 2020 Training set: 339 images (143 MM, 196 BN) Test set:232 images (55 MM, 177 BN)	QuantusSKIN (CNN)	AUC: 0,813 Sen: 0,691 Spe: 0,802 Acc: 0,776
Brinker et al.[41]	Classification (Melanoma/Nevi)	Training set: 12,378 dermoscopic images from ISIC dataset [52] Test set:100 dermoscopic images (20 MM, 80 Nevi)	ResNet-50 (CNN)	Algorithm: Sen: 74.1% Spe: 86.5% Dermatologists: Sen: 74.1% Spe: 60%
Giulini et al.[42]	Classification (Melanoma/Nevi)	Over 28,000 dermoscopic images CNN test set: 2489 images (344 melanomas, 2155 nevi) Physician test set: 100 images (50 MM, 50 nevi)	Session 1: Physicians without CNN Session 2: Physicians with CNN	Physicians without CNN Sen: 56.31% Spe: 69.28% Physicians with CNN Sen: 67.88% Spe: 73.72%
Ding et al.[58]	Classification (Binary:melanoma/non-melanoma and multiclass: benign nevi, seborrheic keratosis or melanoma)	ISIC Dataset [52] Training set: 2000 images (374 MM, 254 SK, 1,372 BN) Validation set: 150 images (30 MM, 42 SK, 78 BN) Test set: 600 images (117 MM, 90 SK, 393 BN)	Segmentation: U-Net Classification: Five CNNs (Inception-v3, ResNet-50, Densenet169, Inception-ResNet-v2 and Xception) with SE-block and the neural network for ensemble learning consisting of two local connected layers and a softmax layer	Binary: Inception-v3 Acc: 0.885 AUC: 0.883 ResNet-50 Acc: 0.88 AUC: 0.882 Densenet169 Acc: 0.893 AUC: 0.882 Inception-ResNet-v2 Acc: 0.89 AUC: 0.894 Xception Acc: 0.891 AUC: 0.896 Ensemble Acc:0.909 AUC: 0.911 Multiclass: Inception-v3 Acc: 0.792 AUC: 0.883 ResNet-50 Acc: 0.762 AUC: 0.864 Densenet169 Acc: 0.800 AUC: 0.881 Inception-ResNet-v2 Acc: 0.800 AUC: 0.873 Xception Acc: 0.810 AUC: 0.896 Ensemble Acc: 0.851 AUC: 0.913
Yu L. Et al.[59]	Segmentation and Classification (Benign/Malignant)	ISIC dataset [52] Training set: 900 images Test set: 350 images	FCRN for skin lesion segmentation and very deep residual network for classification	Segmentation: Sen: 0,911 Spe: 0,957 Acc: 0,949 Classification with segmentation: Sen: 0,547 Spe: 0,931 Acc: 0,855
Bisla et al. [60]	Classification (Nevus, SK, MM)	Training set: ISIC dataset [52]: 803 MM, 2107 nevus, 288 SK PH² dataset [54]: 40 MM, 80 Nevus Edinburgh dataset [31]: 76 MM, 331 nevus, 257 SK Test set: ISIC data sets 600 images (117 MM, 90 SK, and 393 nevus)	Segmentation:Modified U-Net (CNN) Augmentation: de-coupled DCGANs Classification:ResNet-50	AUC: 0,915 Acc: 81,6%
Mahbod et al. [43]	Classification (MM/All, SK/All)	ISIC dataset [52] Training: 2037 dermoscopic images (411 MM, 254 SK, 1372 BN)	Feature Extraction: Pretrained CNNs (AlexNet, ResNet-18 and VGG16) Classification: SVM	AUC: 90,69
Bassel et al. [61]	Classification (Benign/Malignant)	ISIC dataset [52]: 1800 images of benign type and 1497 pictures of malignant cancer Training set: 70% of images (1440 benign, 1197 malignant) Test set: 30% of images (360 benign, 300 malignant)	Model 1:Feature Extraction: ResNet50 Model 2:Feature Extraction: VCG-16 Model 3:Feature Extraction: Xception Classification: Stacked CV model (SVM+NN+RF+KNN)	ResNet Model: Acc: 81,6% AUC: 0,818 VCG-16 Model: Acc: 86,5 % AUC: 0,843 Xception Model: Acc: 90,9 AUC: 0,917
Ningrum et al. [44]	Classification (Melanom/benign)	ISIC dataset [52] 900 images Training set: 720 images Validation set: 180 images Test set: 300 (93 malignant, 207 nonmalignant)	Classification: CNN model for images + ANN model for patient metadata .	CNN Acc: 73.69 AUC: 82.4 CNN+ANN Acc: 92.34 AUC: 97.1
Nambisan et al.[62]	Segmentation and classification (Melanoma/Benign)	ISIC dataset [52] Segmentation task: 487 MM images Classification task: 1000 images (500 MM, and 500 benign (100 images per class from the Actinic keratosis, Melanocytic nevus, Benign keratosis, Dermatofibroma, and Vascular lesion)	Segmentation (Classification dataset+Segmentation dataset (Irregular networks)) U-Net/U-Net++/MA-Net/PA-Net Handcrafted Feature Extraction Classification: Level 0 (without segmentation): DL classification model Level 1 (With segmentation and with level 0 model’s results): Conventional classification model	Conventional Ensemble Acc: 0.793 DL Ensemble Acc: 0.838 EfficientNet-B0 + Conventional Ensemble Acc: 0.862
Collenne et al. [63]	Classification (Melanoma/Nevi)	ISIC dataset [52] (6371 nevi and 1301 melanoma) Training set 70% of images: Validation set: 10% of images Test set: 20% of images	Segmentation: U-Net Classification ANN( for asymmetry features + CNN (EfficientNet)	Handcrafted Model with asymmetry features (ANN): Acc: 79% AUC: 0.87 Sen: 90% Spe: 67% ANN+CNN: Sen: 0.92 Spe: 0.82 Acc: 0.87 AUC: 0.942
Hekler et al. [45]	Classification (Melanoma/Nevi)	HAM10000 [64] and BCN20000 [65] Datasets 29,562 images (7,794 melanoma and 21,768 nevi) %80 training, %20 validation Test set: SCP2 dataset, 293 melanoma and 363 melanocytic nevi from 617 patients	ConvNeXT architecture 1. Classification using single image 2. Classification using multiple real-world images 3. Classification using multiple artificially modified images	Single image approach: Acc: 0.905 ECE: 0.131 Multiview real-world approach: Acc: 0.930 ECE: 0.072 Multiview artificial approach: Acc:0.929 ECE: 0.086
Crawford et al.[46]	Classification (Excision/no excision)	Self-referred patients	MoleAnalyzer Pro	AI Sen: 64.7% Spe: 75.76% PPV: 40.0% NPV: 89.6% Acc: 73.56%

Artificial Neural Network (ANN); Levenberg-Marquardt (LM); Resilient Back propagation (RP); Scaled Conjugate Gradient (SCG); Accuracy (Acc); Sensitivity (Sen); Specificity (Spe); Malign Melanoma (MM); Benign Nevi (BN); Back propagation (BP); International Symposium on Biomedical Imaging (ISBI) challenge 2016; Area under the ROC curve (AUC); International Skin Imaging Collaboration (ISIC); Seborrheic Keratosis (SK); Local Features-Simple Linear Iterative Clustering (LF-SLIC); Scale Invariant Feature Transform (SIFT); Acral Melanoma (AM); Convolutional Neural Network (CNN); Visual Geometry Group (VGG), Squeeze-and-Excitation block (SE-Block); Fully Convolutional Residual Network (FCRN); Deep Convolutional Generative Adversarial Network (DCGAN); Neural network (NN); Random forest (RF); Human Against Machine with 10000 training images (HAM10000); Expected calibration error (ECE); Artificial Intelligence (AI); Negative predictive value (NPV); Positive predictive value ( PPV).

3.2. Distinguishing Melanoma from Other Skin Cancers

Esteva et al. used a pre-trained GoogLeNet Inception v3 architecture and performed transfer learning on 127,463 clinical images, including 3,374 dermoscopy images containing 2032 diseases. After the CNN model was trained, comparisons were made on 135 epidermal (65 malignant, 70 benign), 130 melanocytic (33 malignant, 97 benign), and 111 melanocytic-dermoscopic (71 malignant, 40 benign) images by 21 board-certified dermatologists. CNN performed on par with dermatologists on all 3 criteria. The AUC from clinical photographs was 0.94 for melanoma and 0.91 for melanoma from dermoscopic photographs [66]. Rezvantalab et al. also used pre-trained models (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) in the classification of 8 diagnostic categories (melanoma, melanocytic nevus, BCC, benign keratosis, actinic keratosis, intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevus). All of the models performed better than dermatologists in detecting melanoma and BCC. The most successful model was ResNet 152 with 94.4% AUC in melanoma [67]. Maron et al. included more dermatologists in their study and found that CNNs outperformed dermatologists on both endpoints except BCC [68]. In another study, Tschandl et al. evaluated the success of CNN in nonpigmented cancers, the most common skin cancer manifestation. They trained the model with dermoscopic and clinical images and compared it to 95 human raters. The evaluators were divided into 3 groups: beginner, intermediate, and expert, according to their dermoscopy experience. The model's AUC was higher than the human rating; however, it was less accurate than experts [69]. Tschandl et al. then evaluated the success of ML in benign and malignant pigmented skin lesions. They compared the top 3 algorithms of the ISIC 2018 challenge with human readers and experts and ultimately outperformed both groups [70]. However, these studies only include images of the lesions and do not include clinical information, which has a very important impact on diagnosis. Therefore, a two-level comparison study including textual information was conducted by Haenssle et al. In level I, only dermoscopic images were used, while in level II, clinical and dermoscopic images and textual information were used. At level I, CNN achieved a higher accuracy than dermatologists, but at level II, dermatologists achieved a higher accuracy rate [71].

Although dermatologists and AI are seen as competitors in studies, superior results are often recorded with the combination of classifiers. Hekler et al. investigated the potential benefit of combining humans and AI in skin cancer classification. The primary endpoint was the correct classification of images into five designated categories, while the secondary endpoint was the classification of lesions as benign or malignant. Ultimately, the combination of humans and machines achieved 82.95% accuracy; this was 1.36% higher than the best of the two individual classifiers [72].

Table 3. Algorithms that distinguish melanoma from other skin cancers through dermoscopic images.

Table 3. Algorithms that distinguish melanoma from other skin cancers through dermoscopic images.

Publication	End-point	Dataset	Algorithm	Performance
Esteva et al. [66]	Classification Binary: Keratinocyte carcinoma/SK; melanoma/nevi 3 way: Benign/Malign/Non-neoplastic 9 way: Cutaneous lymphoma and lymphoid infiltrates/ Benign dermal tumors, cysts, sinuses/ Malignant dermal tumor/ Benign epidermal tumors, hamartomas, milia, and growths/ Malignant and premalignant epidermal tumors/ Genodermatoses and supernumerary growths/ Inflammatory conditions/ Benign melanocytic lesions/ Malignant Melanoma	ISIC [52] and Edinburgh dataset [31] and the Stanford Hospital: 129,450 clinical images, including 3,374 dermoscopic images of 757 disease classes Training set: 127,463 images Test set:1,942 images	Google Inception v3 (CNN)	Binary classification (Algorithm AUC) Carcinoma: 0,96 Melanoma: 0,0,94 Melanoma (Dermoscopic images): 0,91 3 way classification: Dermatologist 1 Acc: 65.6% Dermatologist 2 Acc: 66.0% CNN Acc: 69.4 ± 0.8% CNN partitioning algorithm Acc: 72.1 ± 0.9% 9 way classification: Dermatologist 1 Acc: 53.3% Dermatologist 2 Acc: 55.0% CNN Acc: 48.9 ± 1.9% CNN partitioning algorithm Acc: 55.4 ± 1.7%
Rezvantalab et al. [67]	Classification (MM/Melanocytic Nevi/BCC/AKIEC/Benign keratosis/DF/Vascular lesion)	HAM10000 dataset [64] :10015 dermatoscopic images (1113 MM, 6705 nevi, 514 BCC, 327 AK and intraepithelial carcinoma (AKIEC), 1099 benign keratosis, 115 DF, 142 vascular lesions) PH² set (55): 80 nevi, 40 MM Training set: 70 % Validation set: 15% Test set: 15%	Compared CNNs for classification: Inception v3/InceptionResNet v2/ResNet 152/DenseNet 201	AUC (Melanoma) Dermatologist: 82,26 DenseNet 201: 93,80 ResNet 152: 94,40 Inception v3: 93,40 InceptionResNet v2: 93,20 AUC (BCC) Dermatologist: 88,82 DenseNet 201: 99,30 ResNet 152: 99,10 Inception v3: 98,60 InceptionResNet v2: 98,60
Maron et al. [68]	Classification Two way:Benign/Malignant Five way:AKIEC/BCC/MM/Nevi/BKL (benign keratosis, including seborrhoeic keratosis, solar lentigo and lichen planus like keratosis)	Training set: 11,444 images (ISIC Archive[52] and HAM10000 dataset [64]) Test set: 300 test images (60 for each of the five disease classes) (HAM10000 dataset)	CNN (ResNet50)	Two way classification: CNN AUC: 0,928 CNN Spe: 91,3% Dermatologist Spe: 59,8% Five way classification: CNN AUC: 0,960 CNN Spe: 89,2% Dermatologist Spe: 98,8%
Tschandl et al [69]	Classification (Benign/Malignant)	Training set:7895 dermoscopic and 5829 close-up images Test set: 2,072 dermoscopic and close-up images	Combined convolutional neural network (cCNN) (InceptionResNetV2, InceptionV3, Xception, ResNet50)	cCNN: AUC: 0,695 Sen: 80,5% Spe: 53,5% Human Raters: AUC: 0,742 Sen: 77,6% Spe: 51,3%
Tschandl et al. [70]	Classification (7 way classification: intraepithelial carcinoma including AK and Bowen’s disease; BCC; benign keratinocytic lesions including solar lentigo, SK, and LPLK; dermatofibroma; melanoma; melanocytic nevi; and vascular lesions)	Training set: 10,015 dermoscopic images Test set: 1,195 images	Top 3 algorithms of the ISIC 2018 challenge[73]	Algorithms (mean): Sen: 81,9% Spe: 96,2% Human readers (mean): Sen: 67,8% Spe: 94,0%
Haenssle et al.[71]	Classification (Benign/Malignant) Management decision (treatment/ excision, no action, follow-up examination)	Pretrained CNN Test set: 100 images including pigmented/ non-pigmented and melanocytic/non-melanocytic skin lesions	Inception v4/ Moleanalyzer Pro (CNN)	CNN Management Decision: Sen: 95.0% Spe: 76.7% Acc: 84.0% AUC: 0.918 CNN Diagnosis (Benign/Malignant) Sen: 95.0% Spe: 76.7% Acc: 84.0% Level 1 Management Decision: Dermatologist: Sen: 89.0% Spe: 80.7% Acc: 84.0 % Level 1 Diagnosis (Benign/Malignant) Dermatologist: Sen: 83.8% Spe: 77.6% Acc: 80.1% Level 2 Management Decision: Dermatologist: Sen: 94.1% Spe: 80.4% Acc: 85.9% Level 2 Diagnosis (Benign/Malignant) Dermatologist: Sen: 90.6% Spe: 82.4% Acc: 85.7%
Hekler et al. [72]	Primary end point: Classification to 5 categories (MM/nevus/BCC/AK,Bowen’s disease or squamous cell carcinoma/seborrhoeic keratosis, lentigo solaris or lichen ruber planus) Secondary end-point: Binary classification (Benign/malignant)	Training set: 12336 dermoscopic images (585 images of AK,Bowen,SCC, 910 images of BCC, 3101 images of seborrhoeic keratosis,lentigo Solaris,lichen ruber planus, 4219 images of nevi,3521 images of MM)	CNN (ResNet50)	Multiclass classification: Physician Acc: 42.94% CNN Acc: 81.59% Physician+CNN Acc: 82.95% Binary classification: Physician Sen: 66% Spe: 62% CNN Sen: 86.1% Spe: 89.2% Physician+CNN Sen: 89% Spe: 84%
Xinrong Lu et al. [74]	Classification (normal, carcinoma, and melanoma)	HAM10000 dataset [64] Training set: 8012 images (%80) Test set: 2003 images (%20)	Proposed Xception (The ReLU activation function of the model was replaced with the swish activation function) compared with VGG16, InceptionV3, AlexNet and Xception	VGG16: Acc: 48.99 Sen: 53.7 InceptionV3 Acc: 52.99 Sen: 53.99 AlexNet Acc: 75.99 Sen: 76.99 Xception Acc: 92.90 Sen: 91.99 Proposed Xception Acc: 100 Sen: 94.05
Mengistu et al. [75]	Classification (BCC, SCC, MM)	235 images (162 images for training and 73 images for testing)	Combined SOM and RBFNN and compared them with KNN, ANN, and naïve-Bayes	Proposed model Acc: 93,15% KNN Acc:71,23% ANN Acc: 63,01% Naïve-Bayes Acc: 56,16%
Rashid et al. [76]	Classification (MM/Melanocytic Nevus/BCC/AKIEC/Benign Keratosis/DF/Vascular Lesion)	ISIC dataset [52] Training set: 8000 images Test set: 2000 images	GAN compared with CNN (DenseNet and ResNet-50)	GAN Acc: 0,861 DenseNet Acc: 0.815 ResNet-50 Acc: 0.792
Alwakid et al. [77]	Classification (MM/BN/BCC/Vascular lesion/Benign keratosis/Actinic Carcinoma/DF)	HAM10000 dataset [64] 10015 dermatoscopic images Training set: 8029 images Validation set: 993 images Test set: 993 images	Inception-V3, InceptionResnet-V2	Inception-V3 Acc: 0.897 Spe: 0.89 Sen: 0.90 InceptionResnet-V2 Acc: 0.913 Spe: 0.90 Sen: 0.91

Seborrheic Keratosis (SK); International Skin Imaging Collaboration (ISIC); Convolutional Neural Network (CNN); Area under the ROC curve (AUC); Accuracy (Acc); Malign melanoma (MM); Basal Cell Carcinoma (BCC); Squamous Cell Carcinoma (SCC); Actinic keratosis and intraepithelial carcinoma (AKIEC); Dermatofibroma (DF); Actinic keratosis (AK); Human Against Machine with 10000 training images (HAM10000); Benign keratosis, including seborrhoeic keratosis, solar lentigo and lichen planus-like keratosis (BKL); Sensitivity (Sen); Specificity (Spe); Combined convolutional neural network (cCNN); Lichen planus-like keratosis (LPLK); Self-organizing map (SOM); Radial basis function (RBF); Neural network (NN); K-Nearest Neighbors (KNN); Artificial Neural Network (ANN); Generative Adversarial Network (GAN).

The low quality of dermoscopic images and artifacts in the data sets can reduce the success of the models, causing false positives and false negatives or causing overfitting problems. To investigate how much image quality affects the models' success, Maier et al. measured the accuracy and precision of three AI models (AlexNet, ResNet50, and MobileNetv2) in classifying images with different degrees of blurriness and brightness. They report that as blurriness and disparity change, misclassifications occur, model accuracy and improvement decrease, and different models behave differently [78]. Winkler et al. investigated the effect of surgical skin markings, one of the frequently encountered artifacts, on the diagnostic performance of AI models. In unmarked lesions, CNN reached 95.7% sensitivity, 84.1% specificity, and 0.969 AUC, while in marked lesions, specificity decreased to 45.8% and AUC decreased to 0.922 [79]. In another study, the same group investigated the negative impact of the dark corner artifact caused by the tubular lenses of the dermoscope. The small and moderate dark edge artifact did not change the specificity and AUC value, but the large size reduced the specificity and the AUC remained constant [80]. Artifacts such as hair, gel bubbles, and rulers are also frequently encountered in dermoscopic images. Images may need to be preprocessed to avoid reducing the success of the model. However, the image may become corrupted during this process, and this process itself may cause errors [81]. With improvements in segmentation, the impact of these problems on model success is decreasing [82].

4. In Vivo Skin Imaging Devices

5. Conclusions

References

1. Narayanan, D.L.; Saladi, R.N.; Fox, J.L. Ultraviolet radiation and skin cancer. International journal of dermatology 2010, 49, 978–986. [Google Scholar] [CrossRef] [PubMed]
2. Society, A.C. What Is Melanoma Skin Cancer? 2023. [Google Scholar]
3. Society, A.C. Key Statistics for Melanoma Skin Cancer. 2023. [Google Scholar]
4. Rastrelli, M.; Tropea, S.; Rossi, C.R.; Alaibac, M. Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification. In Vivo 2014, 28, 1005–1011. [Google Scholar] [PubMed]
5. Jones, S.; Henry, V.; Strong, E.; Sheriff, S.A.; Wanat, K.; Kasprzak, J.; Clark, M.; Shukla, M.; Zenga, J.; Stadler, M.; et al. Clinical Impact and Accuracy of Shave Biopsy for Initial Diagnosis of Cutaneous Melanoma. J Surg Res 2023, 286, 35–40. [Google Scholar] [CrossRef]
6. Alam, M.; Lee, A.; Ibrahimi, O.A.; Kim, N.; Bordeaux, J.; Chen, K.; Dinehart, S.; Goldberg, D.J.; Hanke, C.W.; Hruza, G.J.; et al. A multistep approach to improving biopsy site identification in dermatology: physician, staff, and patient roles based on a Delphi consensus. JAMA Dermatol 2014, 150, 550–558. [Google Scholar] [CrossRef]
7. St John, J.; Walker, J.; Goldberg, D.; Maloney, M.E. Avoiding Medical Errors in Cutaneous Site Identification: A Best Practices Review. Dermatol Surg 2016, 42, 477–484. [Google Scholar] [CrossRef]
8. Dubois, A.; Levecq, O.; Azimani, H.; Siret, D.; Barut, A.; Suppa, M.; Del Marmol, V.; Malvehy, J.; Cinotti, E.; Rubegni, P.; et al. Line-field confocal optical coherence tomography for high-resolution noninvasive imaging of skin tumors. J Biomed Opt 2018, 23, 1–9. [Google Scholar] [CrossRef]
9. Cinotti, E.; Couzan, C.; Perrot, J.L.; Habougit, C.; Labeille, B.; Cambazard, F.; Moscarella, E.; Kyrgidis, A.; Argenziano, G.; Pellacani, G.; et al. In vivo confocal microscopic substrate of grey colour in melanosis. J Eur Acad Dermatol Venereol 2015, 29, 2458–2462. [Google Scholar] [CrossRef]
10. Jones, O.T.; Matin, R.N.; van der Schaar, M.; Prathivadi Bhayankaram, K.; Ranmuthu, C.K.I.; Islam, M.S.; Behiyat, D.; Boscott, R.; Calanzani, N.; Emery, J.; et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health 2022, 4, e466–e476. [Google Scholar] [CrossRef]
11. Chu, Y.S.; An, H.G.; Oh, B.H.; Yang, S. Artificial Intelligence in Cutaneous Oncology. Frontiers in Medicine 2020, 7. [Google Scholar] [CrossRef] [PubMed]
12. Hogarty, D.T.; Su, J.C.; Phan, K.; Attia, M.; Hossny, M.; Nahavandi, S.; Lenane, P.; Moloney, F.J.; Yazdabadi, A. Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review. Am J Clin Dermatol 2020, 21, 41–47. [Google Scholar] [CrossRef] [PubMed]
13. Patel, S.; Wang, J.V.; Motaparthi, K.; Lee, J.B. Artificial intelligence in dermatology for the clinician. Clin Dermatol 2021, 39, 667–672. [Google Scholar] [CrossRef] [PubMed]
14. Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 1997, 30, 1145–1159. [Google Scholar] [CrossRef]
15. Nahm, F.S. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol 2022, 75, 25–36. [Google Scholar] [CrossRef]
16. Dice, L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26, 297–302. [Google Scholar] [CrossRef]
17. Jaccard, P. The distribution of the flora in the alpine zone. 1. New phytologist 1912, 11, 37–50. [Google Scholar] [CrossRef]
18. Duarte, A.F.; Sousa-Pinto, B.; Azevedo, L.F.; Barros, A.M.; Puig, S.; Malvehy, J.; Haneke, E.; Correia, O. Clinical ABCDE rule for early melanoma detection. European Journal of Dermatology 2021, 31, 771–778. [Google Scholar] [CrossRef]
19. Nasr-Esfahani, E.; Samavi, S.; Karimi, N.; Soroushmehr, S.M.R.; Jafari, M.H.; Ward, K.; Najarian, K. Melanoma detection by analysis of clinical images using convolutional neural network. In Proceedings of the 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2016; pp. 1373–1376. [Google Scholar]
20. Yap, J.; Yolland, W.; Tschandl, P. Multimodal skin lesion classification using deep learning. Experimental dermatology 2018, 27, 1261–1267. [Google Scholar] [CrossRef]
21. Esfahani, P.R.; Mazboudi, P.; Reddy, A.J.; Farasat, V.P.; Guirgus, M.E.; Tak, N.; Min, M.; Arakji, G.H.; Patel, R. Leveraging machine learning for accurate detection and diagnosis of melanoma and nevi: an interdisciplinary study in dermatology. Cureus 2023, 15. [Google Scholar] [CrossRef]
22. Dorj, U.-O.; Lee, K.-K.; Choi, J.-Y.; Lee, M. The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications 2018, 77, 9909–9924. [Google Scholar] [CrossRef]
23. Soenksen, L.R.; Kassis, T.; Conover, S.T.; Marti-Fuster, B.; Birkenfeld, J.S.; Tucker-Schwartz, J.; Naseem, A.; Stavert, R.R.; Kim, C.C.; Senna, M.M. Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Science Translational Medicine 2021, 13, eabb3652. [Google Scholar] [CrossRef] [PubMed]
24. Pomponiu, V.; Nejati, H.; Cheung, N.-M. Deepmole: Deep neural networks for skin mole lesion classification. In Proceedings of the 2016 IEEE international conference on image processing (ICIP); 2016; pp. 2623–2627. [Google Scholar]
25. Han, S.S.; Kim, M.S.; Lim, W.; Park, G.H.; Park, I.; Chang, S.E. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology 2018, 138, 1529–1538. [Google Scholar] [CrossRef] [PubMed]
26. Liu, Y.; Jain, A.; Eng, C.; Way, D.H.; Lee, K.; Bui, P.; Kanada, K.; de Oliveira Marinho, G.; Gallegos, J.; Gabriele, S. A deep learning system for differential diagnosis of skin diseases. Nature medicine 2020, 26, 900–908. [Google Scholar] [CrossRef] [PubMed]
27. Sangers, T.; Reeder, S.; van der Vet, S.; Jhingoer, S.; Mooyaart, A.; Siegel, D.M.; Nijsten, T.; Wakkee, M. Validation of a market-approved artificial intelligence mobile health app for skin cancer screening: a prospective multicenter diagnostic accuracy study. Dermatology 2022, 238, 649–656. [Google Scholar] [CrossRef]
28. Potluru, A.; Arora, A.; Arora, A.; Joiya, S.A. Automated Machine Learning (AutoML) for the Diagnosis of Melanoma Skin Lesions From Consumer-Grade Camera Photos. Cureus 2024, 16. [Google Scholar] [CrossRef]
29. Asan and Hallym Dataset (Thumbnails). 2017. [CrossRef]
30. Giotis, I.; Molders, N.; Land, S.; Biehl, M.; Jonkman, M.F.; Petkov, N. MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert systems with applications 2015, 42, 6578–6585. [Google Scholar] [CrossRef]
31. Ballerini, L.; Fisher, R.B.; Aldridge, B.; Rees, J. A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. Color medical image analysis 2013, 63–86. [Google Scholar]
32. DermIS.
33. Boer, A.; Nischal, K. www. derm101. com: A growing online resource for learning dermatology and dermatopathology. Indian Journal of Dermatology, Venereology and Leprology 2007, 73, 138. [Google Scholar] [CrossRef]
34. Kato, J.; Horimoto, K.; Sato, S.; Minowa, T.; Uhara, H. Dermoscopy of melanoma and non-melanoma skin cancers. Frontiers in medicine 2019, 6, 180. [Google Scholar] [CrossRef] [PubMed]
35. Masood, A.; Al-Jumaily, A.A.; Adnan, T. Development of automated diagnostic system for skin cancer: Performance analysis of neural network learning algorithms for classification. In Proceedings of the Artificial Neural Networks and Machine Learning–ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, 15-19 September 2014; Proceedings 24. pp. 837–844. [Google Scholar]
36. Marchetti, M.A.; Codella, N.C.; Dusza, S.W.; Gutman, D.A.; Helba, B.; Kalloo, A.; Mishra, N.; Carrera, C.; Celebi, M.E.; DeFazio, J.L. Results of the 2016 international skin imaging collaboration isbi challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. Journal of the American Academy of Dermatology 2018, 78, 270. [Google Scholar] [CrossRef] [PubMed]
37. Marchetti, M.A.; Liopyris, K.; Dusza, S.W.; Codella, N.C.; Gutman, D.A.; Helba, B.; Kalloo, A.; Halpern, A.C.; Soyer, H.P.; Curiel-Lewandrowski, C. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017. Journal of the American Academy of Dermatology 2020, 82, 622–627. [Google Scholar] [CrossRef]
38. Yu, C.; Yang, S.; Kim, W.; Jung, J.; Chung, K.-Y.; Lee, S.W.; Oh, B. Acral melanoma detection using a convolutional neural network for dermoscopy images. PloS one 2018, 13, e0193321. [Google Scholar]
39. Abbas, Q.; Ramzan, F.; Ghani, M.U. Acral melanoma detection using dermoscopic images and convolutional neural networks. Vis Comput Ind Biomed Art 2021, 4, 25. [Google Scholar] [CrossRef]
40. Fink, C.; Blum, A.; Buhl, T.; Mitteldorf, C.; Hofmann-Wellenhof, R.; Deinlein, T.; Stolz, W.; Trennheuser, L.; Cussigh, C.; Deltgen, D. Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas. Journal of the European Academy of Dermatology and Venereology 2020, 34, 1355–1361. [Google Scholar] [CrossRef]
41. Brinker, T.J.; Hekler, A.; Enk, A.H.; Klode, J.; Hauschild, A.; Berking, C.; Schilling, B.; Haferkamp, S.; Schadendorf, D.; Holland-Letz, T. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer 2019, 113, 47–54. [Google Scholar] [CrossRef]
42. Giulini, M.; Goldust, M.; Grabbe, S.; Ludwigs, C.; Seliger, D.; Karagaiah, P.; Schepler, H.; Butsch, F.; Weidenthaler-Barth, B.; Rietz, S. Combining artificial intelligence and human expertise for more accurate dermoscopic melanoma diagnosis: A 2-session retrospective reader study. Journal of the American Academy of Dermatology 2024, 90, 1266–1268. [Google Scholar] [CrossRef]
43. Mahbod, A.; Schaefer, G.; Wang, C.; Ecker, R.; Ellinge, I. Skin lesion classification using hybrid deep neural networks. In Proceedings of the ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2019; pp. 1229–1233.
44. Ningrum, D.N.A.; Yuan, S.-P.; Kung, W.-M.; Wu, C.-C.; Tzeng, I.-S.; Huang, C.-Y.; Li, J.Y.-C.; Wang, Y.-C. Deep learning classifier with patient’s metadata of dermoscopic images in malignant melanoma detection. Journal of multidisciplinary healthcare 2021, 877–885. [Google Scholar] [CrossRef]
45. Hekler, A.; Maron, R.C.; Haggenmüller, S.; Schmitt, M.; Wies, C.; Utikal, J.S.; Meier, F.; Hobelsberger, S.; Gellrich, F.F.; Sergon, M. Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning. Journal of the American Academy of Dermatology 2024, 90, 1028–1031. [Google Scholar] [CrossRef]
46. Crawford, M.E.; Kamali, K.; Dorey, R.A.; MacIntyre, O.C.; Cleminson, K.; MacGillivary, M.L.; Green, P.J.; Langley, R.G.; Purdy, K.S.; DeCoste, R.C. Using artificial intelligence as a melanoma screening tool in self-referred patients. Journal of Cutaneous Medicine and Surgery 2024, 28, 37–43. [Google Scholar] [CrossRef] [PubMed]
47. Chanda, T.; Hauser, K.; Hobelsberger, S.; Bucher, T.-C.; Garcia, C.N.; Wies, C.; Kittler, H.; Tschandl, P.; Navarrete-Dechent, C.; Podlipnik, S. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nature Communications 2024, 15, 524. [Google Scholar] [CrossRef] [PubMed]
48. Correia, M.; Bissoto, A.; Santiago, C.; Barata, C. XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision. arXiv preprint 2024, arXiv:2402.01410. [Google Scholar]
49. Aswin, R.; Jaleel, J.A.; Salim, S. Hybrid genetic algorithm—Artificial neural network classifier for skin cancer detection. In Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2017; pp. 1304–1309. [Google Scholar]
50. Xie, F.; Fan, H.; Li, Y.; Jiang, Z.; Meng, R.; Bovik, A. Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model. IEEE Trans Med Imaging 2017, 36, 849–858. [Google Scholar] [CrossRef] [PubMed]
51. Gutman, D.; Codella, N.C.; Celebi, E.; Helba, B.; Marchetti, M.; Mishra, N.; Halpern, A. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint 2016, arXiv:1605.01397. [Google Scholar]
52. ISIC Archive.
53. Cueva, W.F.; Muñoz, F.; Vásquez, G.; Delgado, G. Detection of skin cancer” Melanoma” through computer vision. In Proceedings of the 2017 IEEE XXIV International Conference on Electronics, 2017, Electrical Engineering and Computing (INTERCON); pp. 1–4.
54. Mendonca, T.; Ferreira, P.M.; Marques, J.S.; Marcal, A.R.; Rozeira, J. PH² - a dermoscopic image database for research and benchmarking. Annu Int Conf IEEE Eng Med Biol Soc 2013, 2013, 5437–5440. [Google Scholar] [CrossRef] [PubMed]
55. Navarro, F.; Escudero-Vinolo, M.; Bescós, J. Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE journal of biomedical and health informatics 2018, 23, 501–508. [Google Scholar] [CrossRef]
56. Phillips, M.; Marsden, H.; Jaffe, W.; Matin, R.N.; Wali, G.N.; Greenhalgh, J.; McGrath, E.; James, R.; Ladoyanni, E.; Bewley, A. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA network open 2019, 2, e1913436–e1913436. [Google Scholar] [CrossRef]
57. Martin-Gonzalez, M.; Azcarraga, C.; Martin-Gil, A.; Carpena-Torres, C.; Jaen, P. Efficacy of a deep learning convolutional neural network system for melanoma diagnosis in a hospital population. International Journal of Environmental Research and Public Health 2022, 19, 3892. [Google Scholar] [CrossRef]
58. Ding, J.; Song, J.; Li, J.; Tang, J.; Guo, F. Two-stage deep neural network via ensemble learning for melanoma classification. Frontiers in Bioengineering and Biotechnology 2022, 9, 758495. [Google Scholar] [CrossRef]
59. Yu, L.; Chen, H.; Dou, Q.; Qin, J.; Heng, P.-A. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 2016, 36, 994–1004. [Google Scholar] [CrossRef] [PubMed]
60. Bisla, D.; Choromanska, A.; Berman, R.S.; Stein, J.A.; Polsky, D. Towards automated melanoma detection with deep learning: Data purification and augmentation. In Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops,; 2019. [Google Scholar]
61. Bassel, A.; Abdulkareem, A.B.; Alyasseri, Z.A.A.; Sani, N.S.; Mohammed, H.J. Automatic malignant and benign skin cancer classification using a hybrid deep learning approach. Diagnostics 2022, 12, 2472. [Google Scholar] [CrossRef]
62. Nambisan, A.K.; Maurya, A.; Lama, N.; Phan, T.; Patel, G.; Miller, K.; Lama, B.; Hagerty, J.; Stanley, R.; Stoecker, W.V. Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks. Cancers (Basel) 2023, 15. [Google Scholar] [CrossRef]
63. Collenne, J.; Monnier, J.; Iguernaissi, R.; Nawaf, M.; Richard, M.A.; Grob, J.J.; Gaudy-Marqueste, C.; Dubuisson, S.; Merad, D. Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection. J Invest Dermatol 2024, 144, 1600–1607. [Google Scholar] [CrossRef] [PubMed]
64. Tschandl, P. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. 2018. [Google Scholar] [CrossRef]
65. Hernández-Pérez, C.; Combalia, M.; Podlipnik, S.; Codella, N.C.F.; Rotemberg, V.; Halpern, A.C.; Reiter, O.; Carrera, C.; Barreiro, A.; Helba, B.; et al. BCN20000: Dermoscopic Lesions in the Wild. Scientific Data 2024, 11, 641. [Google Scholar] [CrossRef] [PubMed]
66. Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017, 542, 115–118. [Google Scholar] [CrossRef]
67. Rezvantalab, A.; Safigholi, H.; Karimijeshni, S. Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. arXiv preprint 2018, arXiv:1810.10348. [Google Scholar]
68. Maron, R.C.; Weichenthal, M.; Utikal, J.S.; Hekler, A.; Berking, C.; Hauschild, A.; Enk, A.H.; Haferkamp, S.; Klode, J.; Schadendorf, D.; et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur J Cancer 2019, 119, 57–65. [Google Scholar] [CrossRef]
69. Tschandl, P.; Rosendahl, C.; Akay, B.N.; Argenziano, G.; Blum, A.; Braun, R.P.; Cabo, H.; Gourhant, J.Y.; Kreusch, J.; Lallas, A.; et al. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol 2019, 155, 58–65. [Google Scholar] [CrossRef]
70. Tschandl, P.; Codella, N.; Akay, B.N.; Argenziano, G.; Braun, R.P.; Cabo, H.; Gutman, D.; Halpern, A.; Helba, B.; Hofmann-Wellenhof, R.; et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol 2019, 20, 938–947. [Google Scholar] [CrossRef] [PubMed]
71. Haenssle, H.A.; Fink, C.; Toberer, F.; Winkler, J.; Stolz, W.; Deinlein, T.; Hofmann-Wellenhof, R.; Lallas, A.; Emmert, S.; Buhl, T.; et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol 2020, 31, 137–143. [Google Scholar] [CrossRef] [PubMed]
72. Hekler, A.; Utikal, J.S.; Enk, A.H.; Hauschild, A.; Weichenthal, M.; Maron, R.C.; Berking, C.; Haferkamp, S.; Klode, J.; Schadendorf, D.; et al. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer 2019, 120, 114–121. [Google Scholar] [CrossRef] [PubMed]
73. Codella, N.; Rotemberg, V.; Tschandl, P.; Celebi, M.E.; Dusza, S.; Gutman, D.; Helba, B.; Kalloo, A.; Liopyris, K.; Marchetti, M. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint 2019, arXiv:1902.03368. [Google Scholar]
74. Lu, X.; Firoozeh Abolhasani Zadeh, Y.A. Deep Learning-Based Classification for Melanoma Detection Using XceptionNet. J Healthc Eng 2022, 2022, 2196096. [Google Scholar] [CrossRef]
75. Mengistu, A.D.; Alemayehu, D.M. Computer vision for skin cancer diagnosis and recognition using RBF and SOM. International Journal of Image Processing (IJIP) 2015, 9, 311–319. [Google Scholar]
76. Rashid, H.; Tanveer, M.A.; Khan, H.A. Skin lesion classification using GAN based data augmentation. In Proceedings of the 2019 41St annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2019; pp. 916–919. [Google Scholar]
77. Alwakid, G.; Gouda, W.; Humayun, M.; Jhanjhi, N.Z. Diagnosing Melanomas in Dermoscopy Images Using Deep Learning. Diagnostics 2023, 13. [Google Scholar] [CrossRef]
78. Maier, K.; Zaniolo, L.; Marques, O. Image quality issues in teledermatology: A comparative analysis of artificial intelligence solutions. J Am Acad Dermatol 2022, 87, 240–242. [Google Scholar] [CrossRef]
79. Winkler, J.K.; Fink, C.; Toberer, F.; Enk, A.; Deinlein, T.; Hofmann-Wellenhof, R.; Thomas, L.; Lallas, A.; Blum, A.; Stolz, W.; et al. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. JAMA Dermatol 2019, 155, 1135–1141. [Google Scholar] [CrossRef]
80. Sies, K.; Winkler, J.K.; Fink, C.; Bardehle, F.; Toberer, F.; Kommoss, F.K.F.; Buhl, T.; Enk, A.; Rosenberger, A.; Haenssle, H.A. Dark corner artefact and diagnostic performance of a market-approved neural network for skin cancer classification. J Dtsch Dermatol Ges 2021, 19, 842–850. [Google Scholar] [CrossRef]
81. Sultana, N.N.; Puhan, N.B. Recent deep learning methods for melanoma detection: a review. In Proceedings of the Mathematics and Computing: 4th International Conference, ICMC 2018, Varanasi, India, 9-11 January 2018; Revised Selected Papers. pp. 118–132. [Google Scholar]
82. Jafari, M.H.; Karimi, N.; Nasr-Esfahani, E.; Samavi, S.; Soroushmehr, S.M.R.; Ward, K.; Najarian, K. Skin lesion segmentation in clinical images using deep learning. In Proceedings of the 2016 23rd International conference on pattern recognition (ICPR); 2016; pp. 337–342. [Google Scholar]
83. Atak, M.F.; Farabi, B.; Navarrete-Dechent, C.; Rubinstein, G.; Rajadhyaksha, M.; Jain, M. Confocal microscopy for diagnosis and management of cutaneous malignancies: clinical impacts and innovation. Diagnostics 2023, 13, 854. [Google Scholar] [CrossRef] [PubMed]
84. Kose, K.; Bozkurt, A.; Alessi-Fox, C.; Brooks, D.H.; Dy, J.G.; Rajadhyaksha, M.; Gill, M. Utilizing machine learning for image quality assessment for reflectance confocal microscopy. Journal of Investigative Dermatology 2020, 140, 1214–1222. [Google Scholar] [CrossRef] [PubMed]
85. Gerger, A.; Wiltgen, M.; Langsenlehner, U.; Richtig, E.; Horn, M.; Weger, W.; Ahlgrimm-Siess, V.; Hofmann-Wellenhof, R.; Samonigg, H.; Smolle, J. Diagnostic image analysis of malignant melanoma in in vivo confocal laser-scanning microscopy: a preliminary study. Skin Research and Technology 2008, 14, 359–363. [Google Scholar] [CrossRef]
86. Koller, S.; Wiltgen, M.; Ahlgrimm-Siess, V.; Weger, W.; Hofmann-Wellenhof, R.; Richtig, E.; Smolle, J.; Gerger, A. In vivo reflectance confocal microscopy: automated diagnostic image analysis of melanocytic skin tumours. Journal of the European Academy of Dermatology and Venereology 2011, 25, 554–558. [Google Scholar] [CrossRef]
87. Wodzinski, M.; Skalski, A.; Witkowski, A.; Pellacani, G.; Ludzik, J. Convolutional neural network approach to classify skin lesions using reflectance confocal microscopy. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019; pp. 4754–4757. [Google Scholar]
88. Kose, K.; Bozkurt, A.; Alessi-Fox, C.; Gill, M.; Longo, C.; Pellacani, G.; Dy, J.G.; Brooks, D.H.; Rajadhyaksha, M. Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net). Medical image analysis 2021, 67, 101841. [Google Scholar] [CrossRef]
89. D’Alonzo, M.; Bozkurt, A.; Alessi-Fox, C.; Gill, M.; Brooks, D.H.; Rajadhyaksha, M.; Kose, K.; Dy, J.G. Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels. Scientific Reports 2021, 11, 3679. [Google Scholar] [CrossRef]
90. Herbert, S.; Valon, L.; Mancini, L.; Dray, N.; Caldarelli, P.; Gros, J.; Esposito, E.; Shorte, S.L.; Bally-Cuif, L.; Aulner, N. LocalZProjector and DeProj: a toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images. BMC biology 2021, 19, 1–13. [Google Scholar] [CrossRef] [PubMed]
91. Mandal, A.; Priyam, S.; Chan, H.H.; Gouveia, B.M.; Guitera, P.; Song, Y.; Baker, M.A.B.; Vafaee, F. Computer-aided diagnosis of melanoma subtypes using reflectance confocal images. Cancers 2023, 15, 1428. [Google Scholar] [CrossRef]
92. Gambichler, T.; Jaedicke, V.; Terras, S. Optical coherence tomography in dermatology: technical and clinical aspects. Archives of dermatological research 2011, 303, 457–473. [Google Scholar] [CrossRef]
93. Sattler, E.; Kästle, R.; Welzel, J. Optical coherence tomography in dermatology. Journal of biomedical optics 2013, 18, 061224–061224. [Google Scholar] [CrossRef]
94. Chou, H.-Y.; Huang, S.-L.; Tjiu, J.-W.; Chen, H.H. Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning. Computerized Medical Imaging and Graphics 2021, 87, 101833. [Google Scholar] [CrossRef] [PubMed]
95. Silver, F.H.; Mesica, A.; Gonzalez-Mercedes, M.; Deshmukh, T. Identification of Cancerous Skin Lesions Using Vibrational Optical Coherence Tomography (VOCT): Use of VOCT in Conjunction with Machine Learning to Diagnose Skin Cancer Remotely Using Telemedicine. Cancers 2022, 15, 156. [Google Scholar] [CrossRef] [PubMed]
96. Lee, J.; Beirami, M.J.; Ebrahimpour, R.; Puyana, C.; Tsoukas, M.; Avanaki, K. Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm. Skin Research and Technology 2023, 29, e13377. [Google Scholar] [CrossRef] [PubMed]
97. You, C.; Yi, J.-Y.; Hsu, T.-W.; Huang, S.-L. Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning. Journal of Biomedical Optics 2023, 28, 096005–096005. [Google Scholar] [CrossRef] [PubMed]