Imperial College London
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DrGuangYang

Faculty of Engineering, Department of Bioengineering

Senior Lecturer
 
 
 
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Contact

 

g.yang Website

 
 
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Location

 

229Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hasan:2023:10.1016/j.compbiomed.2023.106624,
author = {Hasan, K and Ahamad, MA and Yap, CH and Yang, G},
doi = {10.1016/j.compbiomed.2023.106624},
journal = {Computers in Biology and Medicine},
pages = {1--36},
title = {A survey, review, and future trends of skin lesion segmentation and classification},
url = {http://dx.doi.org/10.1016/j.compbiomed.2023.106624},
volume = {155},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Hasan,K
AU  - Ahamad,MA
AU  - Yap,CH
AU  - Yang,G
DO  - 10.1016/j.compbiomed.2023.106624
EP  - 36
PY  - 2023///
SN  - 0010-4825
SP  - 1
TI  - A survey, review, and future trends of skin lesion segmentation and classification
T2  - Computers in Biology and Medicine
UR  - http://dx.doi.org/10.1016/j.compbiomed.2023.106624
UR  - https://www.sciencedirect.com/science/article/pii/S0010482523000896
UR  - http://hdl.handle.net/10044/1/102856
VL  - 155
ER  -
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