Analysis of skin lesion images via visual inspection and manual examination to diagnose skin cancer has always been cumbersome. This manual examination of skin lesions in order to detect melanoma can be time-consuming and tedious. With the advancement in technology and rapid increase in computational resources, various machine learning techniques and deep learning models have emerged for the analysis of medical images most especially the skin lesion images. The results of these models have been impressive, however analysis of skin lesion images with these techniques still experiences some challenges due to the unique and complex features of the skin lesion images. This work presents a comprehensive survey of techniques that have been used for detecting skin cancer from skin lesion images. The paper is aimed to provide an up-to-date survey that will assist investigators in developing efficient models that automatically and accurately detects melanoma from skin lesion images. The paper is presented in five folds: First, we identify the challenges in detecting melanoma from skin lesions. Second, we discuss the pre-processing and segmentation techniques of skin lesion images. Third, we make comparative analysis of the state-of-the-arts. Fourth we discuss classification techniques for classifying skin lesions into different classes of skin cancer. We finally explore and analyse the performance of the state-of-the-arts methods employed in popular skin lesion image analysis competitions and challenges of ISIC 2018 and 2019. Application of ensemble deep learning models on well pre-processed and segmented images results in better classification performance of the skin lesion images.

中文翻译：

---

用于皮肤病变分析和黑色素瘤癌症检测的深度学习技术：最新技术调查

通过目视检查和人工检查分析皮肤病变图像以诊断皮肤癌一直很麻烦。这种手动检查皮肤病变以检测黑色素瘤可能既费时又乏味。随着技术的进步和计算资源的快速增加，出现了各种机器学习技术和深度学习模型，用于分析医学图像，尤其是皮肤病变图像。这些模型的结果令人印象深刻，但是由于皮肤病变图像的独特和复杂特征，使用这些技术分析皮肤病变图像仍然面临一些挑战。这项工作对用于从皮肤病变图像中检测皮肤癌的技术进行了全面调查。该论文旨在提供最新调查，帮助研究人员开发有效模型，自动准确地从皮肤病变图像中检测黑色素瘤。该论文分为五个部分：首先，我们确定了从皮肤病变中检测黑色素瘤的挑战。其次，我们讨论了皮肤病变图像的预处理和分割技术。第三，我们对现有技术进行比较分析。第四，我们讨论将皮肤病变分为不同类别的皮肤癌的分类技术。我们最终探索和分析了流行的皮肤病变图像分析竞赛和 ISIC 2018 和 2019 挑战中采用的最先进方法的性能。