Nazari Sana, Garcia Rafael
Computer Vision and Robotics Group, University of Girona, 17003 Girona, Spain.
Life (Basel). 2023 Oct 26;13(11):2123. doi: 10.3390/life13112123.
Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient.
在过去十年中，皮肤癌变得越来越常见，其中黑色素瘤是最具侵袭性的类型。因此，皮肤癌和黑色素瘤的早期检测在皮肤病学中至关重要。计算方法可以成为协助皮肤科医生识别皮肤癌的宝贵工具。由于存在更大的图像数据集，机器学习在皮肤癌检测方面的大多数研究都集中在皮肤镜图像上。然而，全科医生通常无法使用皮肤镜，必须依靠肉眼检查或标准临床图像。通过使用标准的现成相机来检测高危痣，机器学习也已被证明是一种有效的工具。本文的目的是对使用临床图像进行皮肤癌检测的图像处理技术进行全面综述。在本研究中，我们评估了过去十年中使用机器学习方法检测皮肤癌的51篇前沿文章，重点关注临床数据集。尽管该领域已经进行了多项研究，但与现有的皮肤镜数据库相比，仍然很少有公开可用的、具有足够数据的临床数据集可作为基准。此外，我们观察到，现有的伪影去除方法在某些情况下并不十分充分，并且可能对模型也有负面影响。此外，大多数综述文章处理的是单病变图像，没有考虑每个患者病变中的典型痣模式和时间变化。
