Machine Learning Approaches to Skin Lesion Classification and Skin Cancer Detection

The human skin is the biggest organ, and skin diseases can be among the most dangerous forms of illness. Due to genetic disorders, the human body's pathogenic variations might result in abnormal cell development due to which changes occurring in human skin cells become extremely risky. Since skin cancer has a high death rate, timely identification and treatment of skin lesions are essential. However, challenges remain in using Deep learning for analyzing Dermoscopy images, that includes inconsistent image acquisition environment, noise filtering, image segmentation etc., These concerns have led to the suggestion of numerous early detection techniques for skin cancer. This study initially provides a brief review on the latest artificial intelligence methods for predicting skin cancer, including both conventional and deep learning techniques. Further, it discusses the elements of utilizing machine learning approaches for skin tumor identification. The focus of this research is based on the techniques used for segmenting and identifying skin lesions that aims to identify the exact borders and reliably classify them using machine learning (ML). Unbalanced datasets and a lack of data further complicate the development of trustworthy models that can generalize successfully across different skin types and lesion classifications. To get over these limitations and improve the therapeutic advantages of AI-driven skin lesion classification The study assesses widely used Deep learning techniques for skin lesion identification, focusing on key performance metrics such as accuracy, precision, recall, specificity, misclassification rate, and F1 score.