A survey, review, and future trends of skin lesion segmentation and classification.

Autor: Hasan MK; Department of Bioengineering, Imperial College London, UK; Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh. Electronic address: k.hasan22@imperial.ac.uk., Ahamad MA; Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh. Electronic address: asif.fx@live.com., Yap CH; Department of Bioengineering, Imperial College London, UK. Electronic address: c.yap@imperial.ac.uk., Yang G; National Heart and Lung Institute, Imperial College London, UK; Cardiovascular Research Centre, Royal Brompton Hospital, UK. Electronic address: g.yang@imperial.ac.uk.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2023 Mar; Vol. 155, pp. 106624. Date of Electronic Publication: 2023 Feb 01.
DOI: 10.1016/j.compbiomed.2023.106624
Abstrakt: The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate 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 to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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