Rani Suresh Mohadikar
ABSTRACT
The most deadly type of skin cancer, melanoma, poses a serious public health problem, and early detection is essential for enhancing patient outcomes. Deep learning-based classification techniques have recently demonstrated incredible promise in terms of revolutionising melanoma detection. The developments and innovations in this subject are thoroughly explored in this in-depth review. The study starts out by going over the epidemiology of melanoma and its rising prevalence, highlighting the significance of creating reliable and precise detection techniques. It draws attention to the shortcomings of conventional melanoma diagnosis methods and emphasises the potential for deep learning to fill in these gaps. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), Xception, ResNet, among other deep learning models, are examined in detail. The advantages and disadvantages of each strategy are compared, illuminating their applicability for various stages and types of melanoma lesions. As techniques to improve model performance, transfer learning and ensemble approaches are investigated. This promotes robust classification even with small datasets.This paper also discusses the value of augmented data, addressing the dearth of annotated melanoma photos and their implications for deep learning model training. The critical importance of explainable artificial intelligence in fostering transparency and trust in these models is highlighted, with an emphasis on the results' clinical applicability and interpretability. The paper also acknowledges difficulties and constraints like interpretability, model generalisation, and ethical considerations. It is suggested that methods for dealing with these problems be used, including as incorporating dermatological knowledge and continuing research into enhancing model explainability.
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