Review of Skin Cancer Detection and Diagnosis by Applying Different Models of Artificial Intelligence

The use of deep learning and machine learning models for automated skin cancer diagnosis is growing in popularity. Skin conditions that are common and can have life-threatening implications include vascular lesions, actinic keratosis, basal cell carcinoma, melanoma, benign keratosis, and melanocytic nevi. Early identification of skin cancer allows patients to treat the condition and reduces the fatality rate. Skin cancer identification is difficult since diverse skin lesions have many features. Many studies presented a range of advanced models aimed at solving the challenges of automatic skin cancer diagnosis. This article will examine the recently proposed machine learning models developed to identify and categorize skin disorders with different types of skin cancer datasets that have been recently used in many studies and applications, as well as assist readers in understanding the field's limitations and unsolved problems. The performance of many sophisticated machine learning and transfer learning algorithms is assessed in this review study.