Exploring Innovations in Skin Cancer Detection: A Comprehensive Survey using Machine Learning and Deep Learning Approaches - F. More, Bhagyashri; Bhosale, Snehal (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Skin cancer is one of the most prevalent types of cancer in the world, and prompt detection is essential to the effectiveness of therapy. The visual assessment made by dermatologists is a major component of conventional diagnostic approaches, which introduces subjectivity and the possibility of inaccuracy. Machine learning (ML) and deep learning (DL) techniques have emerged as promising tools to enhance the accuracy and efficiency of skin cancer detection. This survey paper provides a comprehensive overview of the recent advancements in the application of ML and DL for skin cancer detection. We analyze the commonly employed ML algorithms, including support vector machines (SVMs), decision trees, and random forests, as well as their performance in skin cancer classification tasks. We then focus on the transformational impact of DL, in analyzing dermoscopic and photographic images for skin lesion identification and segmentation. We systematically review the state-of-the-art ML and DL models, evaluating their accuracy, sensitivity, specificity, and computational efficiency and discuss the critical factors influencing model performance, such as dataset quality, feature engineering strategies, and hyper parameter optimization. Additionally, we address the challenges and limitations of current ML and DL approaches, including issues of data scarcity, model interpretability, and clinical validation. The paper concludes by identifying promising future research directions, emphasizing the need for larger and more diverse datasets, the development of explainable AI models, and the integration of ML and DL systems into clinical workflows to facilitate early and accurate skin identification of skin cancer, ultimately leading to better patient outcomes.