A Comprehensive Review of Machine Learning Techniques for Skin Lesion Detection and Classification

Authors- Mr.N.V.S.Gopalam., D.Gopireddy., R.Rama Naga Ganesh., P.Venkata Satish., R.V.S.Lakshmi Aishwarya., G.Naga Dinesh.

Abstract-This research explores the application of machine learning (ML) in dermatology to enable faster and more accurate identification and classification of skin injuries. Traditional diagnostic methods rely on visual examination, which can be subjective and prone to varying interpretations. ML offers a potential solution by analyzing vast amounts of data and recognizing patterns that enhance diagnostic precision. The study examines current advancements in machine learning, focusing on real-world validation and addressing dataset variability. While deep learning (DL) has shown promising results, the research highlights the advantages of traditional ML techniques in terms of interpretability and processing efficiency. Various approaches for automating skin lesion analysis, including feature engineering, rule-based techniques, and conventional ML algorithms, have been explored. To overcome existing challenges, the study proposes leveraging advanced transfer learning methods, integrating genetic and clinical data, and improving AI explainability. The future of dermatological diagnosis hinges on collaboration between ML experts and dermatologists to develop real-time diagnostic tools that enhance accessibility and accuracy. By combining medical expertise with ML capabilities, this integration has the potential to revolutionize dermatology, offering scalable solutions for rapid lesion diagnosis and improved patient outcomes. The ongoing advancements in automated dermatological diagnostics are expected to pave the way for more personalized treatment approaches, ultimately transforming patient care.

DOI: /10.61463/ijset.vol.13.issue2.271