## 2 The State of the Art  
When we generally talk about the classification process, we mean dividing a large and general set of elements that share common properties into subgroups with more specific shared characteristics. For example, we start with a set whose elements share two properties and arrive at subgroups whose elements share more than two properties. The same principle applies to the diagnosis of skin cancer and skin lesions, a task that is somewhat difficult due to the wide variety of lesions and the absence of a general rule to rely on for classification. In this context, dermatologists utilized the ABCDE criteria for the detection and diagnosis of skin lesions. However, they later realized that these criteria are not always sufficient. The research on the early location of nodular melanoma conducted by [PERSON] et al. [29] showed that nodular melanoma skin lesions do not follow the ABCDE criteria, which proves the limitations of these criteria at times. Later, other methods such as machine learning emerged, but they also faced several challenges, including manual feature extraction, limited accuracy (as these methods were not effective in extracting complex features), high resource requirements for training, and slow processing times. Based on previous limitations, efforts converged to further enhance machine learning, resulting in the emergence of deep learning. Profound learning permits computational models that are made out of various handling layers in view of brain organizations to learn portrayals of information with numerous degrees of reflection [30]. Due to its depth and multiple layers, along with the automation of feature extraction, the ability to learn detailed and complex features which improve model accuracy, and the capability to integrate deep learning models into other applications, deep learning has been able to overcome the obstacles faced by traditional methods. Following the outcome of PC vision, the primary uses of profound learning regarding clinical information involve picture handling, particularly in the examination of attractive reverberation imaging (X-ray) to foresee [PERSON]'s disease and its varieties [31; 32]. The profound structures applied to the medical services area have been, for the most part, founded on convolutional brain organizations (CNNs) [33], intermittent neural networks (RNNs) [34], Restricted Boltzmann Machines (RBMs) [35], and autoencoders (AEs) [36]. The most common one is a CNN. Lately, Convolutional Neural Networks (CNNs) have arisen as the most prevalent architecture among deep learning models, as highlighted in various comprehensive reviews that delve into their applications, advancements, and performance across different domains. [PERSON] et al. emphasized this dominance in their review [37], highlighting CNNs' effectiveness in analyzing clinical images for skin cancer location. Similarly, [PERSON] et al. [38] pointed to CNNs as a cornerstone of deep learning approaches for skin cancer detection, exploring their success in extracting features to separate dangerous and harmless cancers. The review by [PERSON] et al. further underscored this by discussing the dominance of CNN architectures in achieving promising results for skin cancer classification [39]. They delved into the capabilities of CNNs in learning hierarchical features from clinical images, ultimately aiding in cancer detection. A CNN is the most effective learning algorithm for comprehending picture material [31]. CNNs depend on neighborhood associations and tied loads across units followed by pooling (subsampling) to obtain interpretations of invariant descriptors [40]. Several models have been developed based on CNNs, including DenseNet, ResNet, and VGGNet. Regarding the use of CNN models, researchers have the option to use pre-trained models and adapt them to their specific needs with fine-tuning or transfer learning, or to build their own convolutional networks.