Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia.
Microsc Res Tech. 2021 Jun;84(6):1272-1283. doi: 10.1002/jemt.23686. Epub 2021 Jan 5.
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.
皮肤覆盖全身，是人体最大的器官。皮肤癌是最可怕的癌症之一，主要由对太阳紫外线的敏感性引发。然而，风险最大的是黑色素瘤，尽管它有几种不同的发病方式。患者在初始阶段极难识别皮肤恶性肿瘤。文献表明，各种手工和自动深度学习特征被用于使用传统机器和深度学习技术诊断皮肤癌。本研究比较了使用手工和非手工特征的皮肤癌诊断技术。此外，在皮肤癌检测过程中还探索了临床特征，如 Menzies 方法、七点检测、不对称性、边界颜色和直径、视觉纹理（GRC）、局部二值模式、Gabor 滤波器、马尔可夫随机场、分形维数和东方描记术。在基准数据集上比较了几种参数，如提花指数、准确性、骰子效率、精确性、敏感性和特异性，以评估报告的技术。最后，描述了公开的皮肤癌数据集，并强调了剩余的问题。
