## 2 Related Work  
In reference to [15], a CNN classifier was detailed and optimized using metaheuristic techniques for pre-trained network models. The optimization of biases and weights within the CNN models was executed through the implementation of a whale optimization algorithm. Experimental results attained from the application of these algorithms to the Dermquest and DermIS databases showcased superior performance metrics compared to other Deep Learning (DL) techniques. Notably, the achieved metrics included a Sensitivity of 0.95, Specificity of 0.92, and Accuracy of 0.91. Similarly, in the year 2020, [PERSON]. [16] proposed a system integrating an AlexNet CNN designed to detect four distinct classes: Uricaria, Acne, Eczema herpeticum, and Keratosis. The system's training on the DermNetdatabase, followed by testing on 69 images, demonstrated a remarkable accuracy ranging from 98.6% to 99.04% [16]. Another approach surfaced in the work by [17], which introduced an automatic system that employed hyperspectral data and GoogLeNet CNN. This method entailed utilizing a hyperspectral imager to gather position and wavelength information, and GoogLeNet was employed for the classification of skin lesions into two classes. To accommodate the network's three input channels, a \"Mini Network\" layer was added to reduce channel dimensions from 84 to 3. Subsequent evaluation of the system after data augmentation yielded a specificity of 81.2%, accuracy of 77.2%, and sensitivity of 72.3% [17]. For instance, in [16], a CNN comprising three hidden layers with 64, 32, and 16 channels for each layer was proposed. Various optimizers, including Nadam, RMSprop, Adam, and SGD Optimizers, were tested using a learning rate of 0.001. The Adam optimizer emerged as the most effective for skin lesion detection. This system successfully differentiated four distinct classes: melanoma, dermatofibroma, squamous cell carcinoma, and nevus pigments. It achieved an accuracy of 99% and an F1 score close to unity when applied to the ISIC database [18]. In another study, Ref. [19] employed a ResNet-50 CNN trained on 1072 acral benign nevi images from a Korean hospital. The system aimed to detect acral lentiginous melanoma. A comparative analysis was performed across three survey types: dermoscopic images analyzed solely by 60 physicians, dermoscopic images combined with clinical information, and all previous information supplemented by the CNN's predictions. The conclusion drawn indicated that detection performance increased progressively across the stages, culminating in an accuracy of 86.9% [19]. Conducting a comprehensive overview in [20], researchers analyzed 5112 studies on melanoma detection using DL techniques. Out of these, 55 papers were deemed reputable and selected for in-depth analysis. The study offered insights into recent trends, solutions, opportunities, and challenges concerning melanoma detection via DL techniques. The authors also proposed a taxonomy to summarize various detection solutions [20]. Furthermore, a study by [21] delved into CNN models for melanoma detection. The study employed TensorFlow and Keras to build the model, involving varying layers of different network architectures. The system underwent data augmentation, normalization, and transfer learning using CNNs such as InceptionResNet-V2, VGG16, MobileNet, Inception-V3, and ResNet-50. Results demonstrated that InceptionResNet achieved the highest performance, boasting an accuracy, recall, precision, and F1 score of 91%. The system successfully differentiated seven categories of skin lesions [21]. The evaluation of an approved system in the European market for certain types of skin lesions was conducted by [22], revealing favorable diagnostic support from the CNN. However, performance limitations were noted for specific lesion types, such as subungual or mucosal lesions. The study used six dermoscopic image sets, including 100 benign lesions and 30 melanoma images each.