Image Recognition for Detecting Hand Foot and Mouth Disease
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Aryan, Mohammad Farhad; Krathu, Worarat; Arpnikanondt, Chonlameth;
Tassaneetrithep, Boonrat;
Publisher: Hindawi
Publication year: 2020
Start page: 1
End page: 11
Number of pages: 11
ISBN: 9781450377591
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Hand Foot and Mouth Disease is a common childhood skin infection that could quickly develop into a severe case. It spreads easily, with R0 typically above 2. At school, an individual class could be closed for several days. The school could be closed for clean-up. All these closings become an economic burden, especially in the low-income population, that could be prevented or mitigated by a quick response once the disease is first detected. This paper experimented with various combinations of existing image processing and recognition techniques. A state-of-the-art method was discovered to effectively detect lesions of the Hand Foot and Mouth Disease. The results show that color-space conversion as preprocessing followed by segmentation using the KMeans-Morphological process, GLCM and Mean for feature extraction, and Support Vector Machine classifier performed best for the Hand Food and Mount Disease image recognition. © 2020 ACM.
Keywords
Classification, Hand, Foot and Mouth Disease, Image processing, Image recognition
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