Image Recognition for Detecting Hand Foot and Mouth Disease

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.