# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 2 Related Work  
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign [1]. Skin cancer is the most frequent cancer in humans, and its rate is increasing intensely worldwide [2]. Every year, more than 46,000 new cases are recorded in the United Kingdom [4]. Early identification is critical for boosting survival chances, with one study reporting a 90% reduced mortality rate for early-stage skin cancer. However, the present biopsy procedure for diagnosis is laborious and time-consuming. Fortunately, computer technologies provide a viable answer by making it faster, simpler, and more economical to detect skin cancer indications. These systems use clinical pictures and machine learning algorithms to conduct early assessments of suspected skin disorders. Melanoma skin cancer is the most serious and fatal disease in the family of skin cancer diseases [3]. According to studies, the incidence of skin cancer is growing year after year in comparison to various types of cancer [5]. Melanoma, an among the most prevalent kinds of skin cancer, involves melanocytes, cells on the skin's surface that produce pigmentation [6]. It can appear in a variety of dark tints and colors, including rose pink, royal purple, blue, and even colorless [7]. Melanoma's aggressive nature and tendency to spread quickly make it highly dangerous and potentially lethal. Melanoma can appear anywhere on the body; however, it most commonly occurs on the rear of the lower limbs [8]. Skin cancer death rates can be considerably reduced with timely identification and early diagnosis [7;8]. As a result, the early detection and classification of skin cancer are crucial. Dermatologists noted that melanoma may spread to several sites and organs of the human body [9]. As a result, melanoma is the most serious type of skin cancer. As a result, despite its rarity, it is far and away the leading cause of death [10]. The rising frequency of skin cancer can be due to prolonged and direct sun exposure, which can cause the development of both benign and malignant tumors. Melanoma and nevi are both melanocytic tumors, which can lead physicians to misclassify patients based only on visual inspection [11]. Early diagnosis is critical since there is a greater likelihood of successful therapy in the early stages of the disease. However, visual variables such as eye tiredness might impede effective diagnosis, frequently resulting in inaccurate lesion detection [12]. Identifying skin cancer early improves the odds of effective therapy. However, distinguishing between malignant and benign skin lesions and precisely diagnosing skin cancer at these early stages can be difficult. This is because skin lesions commonly resemble one another. Melanoma's early development features are similar to those of other benign moles, making it difficult for even experienced dermatologists to distinguish between what is malignant and what is benign [13]. Researchers are currently examining and evaluating different deep learning algorithms to detect skin diseases. In a study, five machine learning algorithms, namely Random Forest, Kernel SVM, Naive Bayes, Logistic Regression, and Convolutional Neural Networks, were utilized to diagnose skin disorders [14]. According to the results, the Convolutional Neural Network model displayed the best performance in identifying diseases based on the confusion matrix. Another group of researchers proposed an AI system based on neural networks, which involved extracting features during image collection and utilizing a feed-forward neural network for classification. In a separate study, six alternative data mining methods were recommended to classify different types of skin diseases. Additionally, an ensemble approach combining Bagging, AdaBoost, and Gradient Boosting classifiers was developed, demonstrating higher accuracy and effectiveness in detecting skin diseases [14].