@article {10.3844/jcssp.2022.940.954,
article_type = {journal},
title = {A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks},
author = {Reddy, Akepati Sankar and M. P, Gopinath},
volume = {18},
number = {10},
year = {2022},
month = {Oct},
pages = {940-954},
doi = {10.3844/jcssp.2022.940.954},
url = {https://thescipub.com/abstract/jcssp.2022.940.954},
abstract = {Skincancer is a deadly malignancy. Incomplete D.N.A. repair in skin cells causeshereditary mutations and cancer. Early skin cancer is easier to treat since itspreads slowly to other body areas. As a result, the optimal time to find it isduring its infancy. Because of the rising frequency of skin cancer, the highmortality rate, and the high cost of medical treatment, early detection of skincancer symptoms is essential. Researchers have created a variety of earlydetection techniques for skin cancer due to these obstacles. A lesion'ssymmetry, coloration, size, and shape help doctors identify and differentiate between skin cancer and melanoma.These considerations prompted the researcher to do research into automated skincancer diagnosis. The use of machine learning is quickly becoming one of themost promising approaches to the early detection and treatment of skin cancer. Arecent study demonstrated the ability of deep network topologies to segment andanalyzes skin cancer. According to the findings of this study, furtherinvestigation into the application of Deep Learning (DL) algorithms for theearly detection of skin cancer is required. An investigation into significantresearch articles on skin cancer diagnosis that have been published inreputable journals was carried out.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}