Publication: Image Recognition for Detecting Hand Foot and Mouth Disease
Issued Date
2020-07-01
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2-s2.0-85089195373
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Mahidol University
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SCOPUS
Bibliographic Citation
ACM International Conference Proceeding Series. (2020)
Suggested Citation
Mohammad Farhad Aryan, Worarat Krathu, Chonlameth Arpnikanondt, Boonrat Tassaneetrithep Image Recognition for Detecting Hand Foot and Mouth Disease. ACM International Conference Proceeding Series. (2020). doi:10.1145/3406601.3406640 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/57824
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Title
Image Recognition for Detecting Hand Foot and Mouth Disease
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Abstract
© 2020 ACM. 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.