## 2 Related Works  
In biomedical applications, Convolution Neural Network (CNN) models are prominent. hundreds of thousands of articles on CNN-based medical image applications are published every year. The authors [PERSON] et al. [17] proposed a new CADx system to analyze and diagnose melanocytic and non-melanocytic skin lesions. This model was developed using conventional digital photographic images. The model is based on support vector machines to classify lesions as benign or malignant. They concluded that conventional digital micrographs could provide essential information for the CAD system to achieve the desired objectives. CAD system using texture analysis method has been developed for skin lesion detection [18]. The system provides four stages: hair removal, image filtering, feature extraction, and classification techniques. Each stage plays a significant role in skin lesion classification. Additionally, a survey study provided a comprehensive analysis of methods and algorithms that have been proposed to segment and classify the types of skin lesions [16]. [PERSON] [19] used an automated CAD system of skin lesion detection and classification for dermoscopy images. K-means classification was used to cluster and segment the skin lesions. Then, support vector machines (SVM) are applied to classify the types of skin lesions, such as melanoma, basal cell carcinoma (BCC), seborrhoeic keratosis (SK) and Nevus. A CAD system that applies two stages to segment and classifies skin cancer melanoma from skin lesion images is developed [20]. The approach starts with a preprocessing step that removes unwanted noise as well as hair; then, the segmentation stage selects the region of interest using a mean-shift algorithm. The features extraction algorithm is used to obtain features to feed into various classification algorithms such as KNN, a decision tree and SVM. A CAD system for segmentation and classification of skin lesion disease diagnosis was also developed by [PERSON] et al. [21]. In this model, a region-growing algorithm is used for the segmentation while SVM and KNN classifiers were used to predict the skin lesion type. A fully automated CAD system was proposed to segment and classify skin lesions based on deep learning features by [PERSON] and [PERSON] [14]. The method incorporates five essential stages of segmentation and classification. Firstly, contrast stretching is applied with a hybrid approach using local color-controlled histogram intensity values (LCcHIV). Thereafter, a deep saliency-based technique is adopted to compute a saliency map refined through a superpixel technique. After that, images are transformed into binary images, and morphological algorithms are performed to refine the extracted lesions. The majority of the techniques discussed above make use of a large number of parameters which makes deep neural networks more complex. These systems can perform the classification of skin lesions using a moderate number of parameters in the deep convolutional neural network. Some of the architectures discussed in this section are slow and require a significant amount of computing processing power to perform real-time medical analysis and diagnosis. The system proposed here should improve this performance. Finding the optimal hyperparameters for CNN architectures used for skin lesion classification can be challenging due to several factors. One of the factors is the large number of parameters in most CNN architectures, which makes training them computationally expensive and requires a substantial amount of labeled data. There are several approaches researchers have proposed in order to overcome these challenges. One method is a genetic algorithm (GA) for skin lesion classification to optimize the CNN model [22; 23; 24]. Genetic algorithms are inspired by evolution theory and use natural operations such as mutation, crossover, and selection. It determines and selects the fittest individual for the propagation of the search.