Machine Learning Methods in Skin Disease Recognition: A Systematic Review

Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) appl...

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Bibliographic Details
Published in:Processes 2023-04, Vol.11 (4), p.1003
Main Authors: Sun, Jie, Yao, Kai, Huang, Guangyao, Zhang, Chengrui, Leach, Mark, Huang, Kaizhu, Yang, Xi
Format: Article
Language:English
Subjects:
Algorithms
Archives & records
Artificial intelligence
Automation
Classification
Collaboration
Datasets
Deep learning
Dermatology
Diagnosis
Employee motivation
Image classification
Image processing
Image segmentation
Learning algorithms
Lesions
Machine learning
Medical diagnosis
Medical imaging
Melanoma
Recognition
Signs and symptoms
Skin
Skin cancer
Skin diseases
Skin lesions
System effectiveness
Technology application
Texture recognition
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Summary:Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) applications in skin lesion research, with the goal of encouraging the development of automated systems for skin disease diagnosis. To assist dermatologists in their clinical diagnosis, several skin image datasets have been developed and published online. Such efforts have motivated researchers and medical staff to develop automatic skin diagnosis systems using image segmentation and classification processes. This paper summarizes the fundamental steps in skin lesion diagnosis based on papers mainly published since 2013. The applications of ML methods (including traditional ML and deep learning (DL)) in skin disease recognition are reviewed based on their contributions, methods, and achieved results. Such technical analysis is beneficial to the continuing development of reliable and effective computer-aided skin disease diagnosis systems. We believe that more research efforts will lead to the current automatic skin diagnosis studies being used in real clinical settings in the near future.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11041003