Literature Survey on Skin Lesion Classification

Abstract

Abstract: Skin cancer is a growing public health concern, and early detection is crucial for successful treatment. In recent years, advances in deep learning algorithms have led to the development of skin lesion classification systems, which have the potential to improve the accuracy and speed of skin cancer diagnosis. Skin lesion classification is the task of accurately identifying and categorizing different types of skin abnormalities, including moles, freckles, and skin cancers. Deep learning algorithms have demonstrated encouraging outcomes and tremendous capabilities in handling image analysis. It can effectively tackle intricate issues in a prompt and effective way. However, Deep learning models may not always resemble or reflect the decision-making methods of dermatologists. Therefore, an Explainable Artificial Intelligence (XAI) is employed to comprehend the machine learning verdicts via the visual depictions of the categories of skin lesions. This paper provides an overview of the skin lesion classification using a deep learning algorithm along with an explainable artificial intelligence.