A Comprehensive Survey of Skin Cancer Identification using Deep Learning

In USA, out of all the carcinomas, one of the most rampant variety of carcinomas is skin cancer, with an estimated one in five Americans developing it by the age of 70. As per the Skin Cancer Foundation, in the USA alone, every hour more than 2 people succumb to skin cancer. For melanoma skin cancer, the survival rate could be 99% considering a 5-year time frame if it is detected early. Deep learning, a subdomain of AI, empowers computers to learn complex patterns from massive amounts of data. Convolutional neural networks (CNNs), an eminent deep learning architecture, along with its variations like VGG19, MobileNet, ResNet, ResNext, and the latest Vision transformers excel at image recognition tasks, making them ideally suited for analyzing medical images like skin lesions. This review explores the burgeoning utilization of deep learning in skin cancer detection. The analysis of the constraints of conventional methods and highlights of the potential of deep learning in achieving superior accuracy and objectivity have been discussed in this study. The review methodology involves a comprehensive search of relevant research papers and publications from Google Scholar. The review focuses on the studies involving deep learning for classification or segmentation of skin cancer, enabling more efficient and trustworthy AI systems. The findings reveal CNNs as the mainstay, with both traditional training and transfer learning approaches proving effective. However, recent advancements showcase the promise of vision transformers, ensemble methods, and hybrid models, alongside innovative augmentation and optimization techniques, combining attention layers with state-ofthe- art architectures, making clinically trustworthy systems using XAI techniques like GRADCAM, leading to significantly improved efficiency. In conclusion, this review emphasizes the transformative power of deep learning algorithms for the diagnosis of skin cancer, paving the way for highly accurate, trustworthy, and accessible diagnostic tools and presents an analysis of the latest developments related to AI and deep learning architectures and frameworks being applied for diagnosis of skin cancer.