## 2 Related Work
High specificities and sensitivities were achieved for nodular and superficial melanomas, while subungual and mucosa melanoma detection presented lower performance [22]. Transitioning to 2021, [PERSON]. [23] introduced a system incorporating a MobileNet pre-trained CNN for feature extraction. Support Vector Machine (SVM) and linear discriminant analysis (LDA) were utilized for classifying skin lesions into melanoma and non-melanoma classes. By applying cropping and boundary localization to PH2 database images, the proposed system achieved an exceptional specificity of 97.91% and an accuracy of 95% [23]. In a similar vein, [PERSON]. [24] proposed two systems involving a CNN model and a combination of CNN and Neural Network NN. The combined model exhibited higher accuracy (92.34%) compared to the standalone CNN model (73.69%). Performance assessments were conducted on 1200 images from the ISIC database [24]. Additionally, [PERSON]. [25] assessed the performance of their CNN on the HAM10000 database. The system, incorporating 45 layers and utilizing InceptionResNet-V2, achieved an impressive accuracy of 99.69%, with low false negatives. The system accurately classified images into nevi, non-nevi, and melanoma categories [25]. In another study, [PERSON]. [26] focused on pre-processing approaches to enhance a CNN-based skin lesion detection system. Region of interest (ROI) extraction was accomplished using a proposed algorithm, leading to improved CNN model training and evaluation efficiency. InceptionResNet-V2's performance notably improved by 2.18% when trained on ROI images. ISIC-2019 served as the database for this study [26]. In a study by [27], the author employed the EfficientNet-B6 CNN to capture fine-grained features, yielding competitive results with an AUC-ROC score of 0.917 when applied to the ISIC 2020 database [27]. As research extended into 2022, [PERSON]. [28] employed Deep Learning to accurately extract skin lesions. A process involving Super-Resolution Generative Adversarial Networks (ESRGAN) improved image quality, followed by the extraction of Regions of Interest (ROI). Employing a CNN and a modified version of ResNet-50, the proposed method displayed high potential with metrics such as F-score, recall, accuracy, and precision, reaching 0.86, 0.86, 0.86, and 0.84, respectively [28]. Alternatively, [PERSON]. [29] introduced a novel approach considering a third dimension, namely the depth of the skin lesion. This dimension, captured from light-field images, was combined with two-dimensional (2D) information using Morlet scattering transform and CNN. The combined three-dimensional (3D)-two-dimensional approach yielded the highest accuracy of 94%, while 2D-only and 3D-only dimensions achieved accuracies of 84% and 74%, respectively [29]. A novel Fuzzy GrabCut-stacked convolutional neural network GC-SCNN-based model was proposed by [30] for skin lesion detection. Utilizing PH\({}^{2}\) and HAM10000 databases, fuzzy logic enabled boundary detection, and stacked CNNs (GC-SCNN) facilitated feature extraction. Segmentation-enhanced SVMs were then used for segmentation and classification, achieving high metrics, including 100% specificity, sensitivity, and 99.75% accuracy [30]. Continuing into [31], the ISIC 2018 database was leveraged to train and test the proposed method. Pre-processing, subsampling, and down-sampling steps were conducted to balance data, and VGG16 and ResNet-50 CNNs were used to detect melanoma. The system demonstrated a dice coefficient of 97.4%, accuracy of 92.3%, recall of 90%, Jaccard index of 99.8%, and precision of 93.3% [31]. By 2023, [PERSON]. [32] proposed a CNN strategy for early melanoma detection involving five convolution layers, five pooling layers, a fully connected layer, an input layer, and an output layer. Employing 10,000 and 1000 images for training and testing, respectively, the system achieved an accuracy of 91% [33].