Abstract
Burn image classification is critical and attempted problems in medical image processing. This paper proposes the image classification model applied for burn images. The proposal model use one-class Support Vector Machine with color features for burn image classification. The aim of this model is to identify automatically the degrees of burns in three levels: II, III, and IV. The skin burn color images are used as inputs to the model. Then, we apply the multi-color channels extraction and binary based on adaptive threshold for Support Vector Machine classifier. The proposal model uses One- class Support Vector Machine instead of kernel Support Vector Machine because of unbalance degrees of burns images database. The experiments are conducted with the real-life image provided by Cho Ray hospital with the precision 77.78 %. The validation process shows that our main results and the feasibility of our proposal model are stated (Fig. 1) .
Degrees of burn [1]. This shows a figure consisting of four types of burns relating to the depths of skin. The more into the depths, higher degree of burn, for example, as the fourth degree, the burn wounds are into the muscle depth.
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Acknowledgments
The author is greatly indebted to Doctor Vo Van Phuc and his colleges in the burn department of Cho Ray hospital for his helping, guidance, understanding, and most importantly, his expertise during this study.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tran, H., Le, T., Le, T., Nguyen, T. (2016). Burn Image Classification Using One-Class Support Vector Machine. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_23
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DOI: https://doi.org/10.1007/978-3-319-29236-6_23
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