A Survey on Skin Lesion Detection and Classification using Machine Learning

The research explores how dermatologists use machine learning to quickly and accurately identify and classify skin injury. Conventional diagnosis techniques depend on visual examination, but are subjective and have different interpretations. The ability to analyze data and recognize patterns is a possible remedy for ML. The paper examines the current technology of machine learning, focusing on validation in the real world, and deals with data set variability. Although the research acknowledges the fruitfulness of deep learning (DL), the research emphasizes the benefits of traditional ML techniques regarding interpretation and processing performance. Methods for automating the analysis of skin lesions, such as feature engineering, rule-based techniques and traditional ML algorithms, have been studied. The study suggests using advanced transfer learning techniques, integrating genetic and clinical data, and refining the way artificial intelligence (AI) is explained in order to get over barriers. Intending to enhance the accessibility and correctness of skin lesion identification, the future requires collaboration between dermatology and machine learning to develop real-time diagnostic tools. By offering scalable solutions for rapid diagnosis of lesions and improved patient outcomes, this combination of medical expertise and ML capabilities has the power to revolutionize dermatology. The future of automated dermatological diagnosis is expected to be shaped by collaboration between machine learning experts and dermatologists, enabling more personalized treatment for patients.