Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 18, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/ijerph18105479
Keywords
deep learning; deep neural network (DNN); machine learning; melanoma; support vector machine (SVM); skin lesion
Funding
- Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia
Ask authors/readers for more resources
Skin cancer, caused by un-repaired DNA in skin cells, can be cured in initial stages through early detection. Researchers have developed various early detection techniques, including deep learning, to address the increasing rate of skin cancer cases. Analysis of lesion parameters can accurately diagnose skin cancer.
Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available