A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks

Abstract

Skin cancer is a deadly malignancy. Incomplete D.N.A. repair in skin cells causes hereditary mutations and cancer. Early skin cancer is easier to treat since it spreads slowly to other body areas. As a result, the optimal time to find it is during its infancy. Because of the rising frequency of skin cancer, the high mortality rate, and the high cost of medical treatment, early detection of skin cancer symptoms is essential. Researchers have created a variety of early detection techniques for skin cancer due to these obstacles. A lesion's symmetry, coloration, size, and shape help doctors identify and differentiate between skin cancer and melanoma. These considerations prompted the researcher to do research into automated skin cancer diagnosis. The use of machine learning is quickly becoming one of the most promising approaches to the early detection and treatment of skin cancer. A recent study demonstrated the ability of deep network topologies to segment and analyzes skin cancer. According to the findings of this study, further investigation into the application of Deep Learning (DL) algorithms for the early detection of skin cancer is required. An investigation into significant research articles on skin cancer diagno sis that have been published in reputable journals was carried out.

Literature Review: Skin Cancer Types Detection Using Deep Learning

“…The classification method discussed in the paper [7] entails the utilization of algorithms like machine learning or deep learning to categorize lesion images into benign or malignant classes. The authors conduct a thorough survey of various algorithms, also including artificially created neural networks, support vector machines, k-nearest neighbors, random forest, and deep convolutional neural networks, to explore and evaluate their effectiveness in skin cancer detection.…”