## 1 Introduction  
Computer-aided diagnosis (CAD) uses a computer-generated output to assist a clinician in making a diagnosis. CAD can assist doctors in predicting deadly skin cancer diseases where the treatments are accessible and easier to implement at an early stage [1, 2]. Dermatologists have traditionally used two approaches to identify the type of cancer, manual screening, and the use of dermoscopy tools which are magnifiers that can identify more details. Dermatology tools have been used widely to detect melanoma because they can identify melanoma cancer features in the early stages of development. Dermoscopy enables the viewer to detect structures and details that the naked eye cannot see under the epidermis. It provides a better representation of many pigmented structures such as pigment networks, blue-white areas, dots, and streaks, which cannot be observed by the naked eye [3, 4]. Given that skin cancer is not easily identified as melanoma using one feature alone, dermatologists are more confident distinguishing the types of lesions using dermoscopy images. However, there are limitations in using either visual screening or dermoscopy tools to detect skin cancer diseases. Screening of lesion images through manual or dermoscopic methods is tedious, complex, and prone to errors due to the subjective nature of skin lesion images [5, 6]. The complexity of skin lesion images, including elements such as hair, blood vessels, oils, bubbles, and other image noise, make it challenging to identify lesion details unambiguously [7, 8, 9]. Furthermore, low contrast between the surrounding skin and lesion area, along with varying sizes, shapes, and skin lesion colors, hinder the efficiency of these methods [6, 7, 8]. As a result, manual screening has become challenging for dermatologists, and the need for an automatic computerized diagnostic system for accurate skin lesion detection and quick decision-making by dermatologists has become imperative [1; 9; 10; 11]. The need for reliable, robust, and accurate CAD systems is fundamental and necessary. CAD can decrease the time taken for image examination and increase the accuracy of the final diagnosis. The limitations of visual skin lesion screening can be overcome by using a CAD system. The CAD system takes an image of the skin lesion as input and extracts a set of valuable features used to identify the type of skin lesion. Initial feasibility studies of CAD systems of skin lesions concluded that such systems could be a low-cost diagnostic tool [12]. A survey by [PERSON] et al. [13] found that even using a simple computer vision analysis algorithm produced reliable diagnostic results. Recently, with advancements in technology, various CAD systems have been developed to analyze and diagnose skin cancer from medical images. The integration of CAD systems with dermoscopy images has become a significant field of research since they provide meaningful skin lesion information. This integration allows for further improvements in quick and accurate melanoma diagnoses. Thus, the current primary challenge for CAD systems is to develop an efficient automated tool for early disease diagnosis while ensuring a high level of accuracy in their diagnoses [14; 15; 16]. Moreover, research improvements in classifying and detecting skin cancer using CAD systems have evolved rapidly in recent years. However, there is still room for more enhancements due to several challenges [16]. First, the extraction of lesion structures such as pigment networks, region borders, streaks, and globules or dots is complicated and needs more attention. Second, poor image quality, such as intensity inhomogeneity and noise, can result in CAD systems failing to reach the optimal detection results due to the complicated structures of the melanoma. Third, most CAD systems focus on the binary classification of melanoma even though non-melanoma cancer can classify into several types of skin cancers.