Debelee Taye Girma
Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia.
Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia.
Diagnostics (Basel). 2023 Oct 7;13(19):3147. doi: 10.3390/diagnostics13193147.
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
皮肤病变对于多种皮肤病的早期检测和管理至关重要。由于计算机视觉和机器学习技术的进步，基于学习的皮肤病变分析方法近来备受关注。本综述论文介绍了皮肤病变分类、分割和检测的最新方法。本综述论文还讨论了皮肤病变分析在医疗保健中的重要性以及体格检查的困难。然后深入探讨了针对皮肤病变分类的前沿论文综述，目标是从皮肤镜、宏观和其他病变图像格式中正确识别皮肤病变的类型。研究了所选研究论文中使用的各种技术的贡献和局限性，包括深度学习架构和传统机器学习方法。该综述接着研究了专注于皮肤病变分割和检测技术的研究论文，这些技术旨在识别皮肤病变的精确边界并进行相应分类。这些技术便于进行后续分析，并允许进行精确测量和定量评估。该综述论文讨论了著名的分割算法，包括基于深度学习的、基于图形的和基于区域的算法。还讨论了皮肤病变分割特有的困难、数据集和评估指标。在整个综述中，突出了与皮肤病变分析相关的著名数据集、基准挑战和评估指标，对该领域进行了全面概述。本文最后总结了皮肤病变分类、分割和检测的主要趋势、挑战和潜在的未来方向，旨在激发皮肤病学研究这一关键领域的进一步进展。
