Government Associate College for Women Mari Sargodha, Sargodha 40100, Pakistan.
Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan.
Int J Environ Res Public Health. 2021 May 20;18(10):5479. doi: 10.3390/ijerph18105479.
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.
皮肤癌是最危险的癌症形式之一。皮肤癌是由皮肤细胞中未修复的脱氧核糖核酸（DNA）引起的，这些 DNA 在皮肤中产生遗传缺陷或突变。皮肤癌往往会逐渐扩散到身体的其他部位，因此在早期阶段更容易治疗，这就是为什么最好在早期阶段进行检测。皮肤癌病例的增长率、高死亡率和昂贵的医疗费用都要求早期诊断其症状。考虑到这些问题的严重性，研究人员已经开发出各种皮肤癌早期检测技术。使用病变参数，如对称性、颜色、大小、形状等，来检测皮肤癌，并区分良性皮肤癌和黑色素瘤。本文对皮肤癌早期检测的深度学习技术进行了详细的系统综述。分析了发表在有声誉的期刊上、与皮肤癌诊断主题相关的研究论文。研究结果以工具、图表、表格、技术和框架的形式呈现，以便更好地理解。
