Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends

• URL: http://arxiv.org/abs/2208.12232v1
• Date: Thu, 25 Aug 2022 17:31:15 GMT
• Title: Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends
• Authors: Md. Kamrul Hasan, Md. Asif Ahamad, Choon Hwai Yap, Guang Yang
• Abstract summary: The article provides a complete literature review of cutting-edge CAD techniques published between 2011 and 2020. It will guide researchers of all levels, from beginners to experts, in the process of developing an automated and robust CAD system for skin lesion analysis.
• Score: 3.565012455354754
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Computer-aided Diagnosis (CAD) system for skin lesion analysis is an emerging field of research that has the potential to relieve the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists in order to reduce the challenges that are raised by manual inspection. The purpose of this article is to provide a complete literature review of cutting-edge CAD techniques published between 2011 and 2020. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to identify a total of 365 publications, 221 for skin lesion segmentation and 144 for skin lesion classification. These articles are analyzed and summarized in a number of different ways so that we can contribute vital information about the methods for the evolution of CAD systems. These ways include: relevant and essential definitions and theories, input data (datasets utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria (metrics). We also intend to investigate a variety of performance-enhancing methods, including ensemble and post-processing. In addition, in this survey, we highlight the primary problems associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these plights. In conclusion, enlightening findings, recommendations, and trends are discussed for the purpose of future research surveillance in related fields of interest. It is foreseen that it will guide researchers of all levels, from beginners to experts, in the process of developing an automated and robust CAD system for skin lesion analysis.

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