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.

中文翻译：

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使用手工和非手工特征的用于显微皮肤癌诊断的计算机视觉

皮肤覆盖整个身体，是最大的器官。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，尽管它以几种不同的方式开始。患者在初期对识别皮肤恶性生长极为不了解。文献表明，使用传统的机器和深度学习技术，可以使用各种手工和自动深度学习特征来诊断皮肤癌。目前的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，临床特征如孟席斯方法、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、在皮肤癌检测过程中还探索了分形维数和东方直方图。几个参数，如提花指数、准确性、骰子效率、精确度、灵敏度和特异性，在基准数据集上进行比较，以评估报告的技术。最后，描述了公开可用的皮肤癌数据集，并强调了剩余的问题。