A comparative study of features extracted in the classification of human skin burn depth
The first burn treatment provided to patient is usually based on the first evaluation of the skin burn injury by determining the burn depths. In this paper, the objective is to conduct a comparative study of the different set of features extracted and used in the classification of different burn dep...
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Universiti Teknikal Malaysia Melaka
2017
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my.unimas.ir.197162022-09-29T03:31:43Z http://ir.unimas.my/id/eprint/19716/ A comparative study of features extracted in the classification of human skin burn depth Kuan, Pei Nei Li, Stephanie Chua Hui Ehfa, binti Bujang Safawi Q Science (General) R Medicine (General) The first burn treatment provided to patient is usually based on the first evaluation of the skin burn injury by determining the burn depths. In this paper, the objective is to conduct a comparative study of the different set of features extracted and used in the classification of different burn depths by using an image mining approach. Seven sets of global features and 5 local feature descriptors were studied on a skin burn dataset comprising skin burn images categorized into three burn classes by medical experts. The performance of the studied global and local features were evaluated using SMO, JRIP, and J48 on 10-fold cross validation method. The empirical results showed that the best set of features that was able to classify most of the burn depths consisted of mean of lightness, mean of hue, standard deviation of hue, standard deviation of A* component, standard deviation of B* component, and skewness of lightness with an average accuracy of 77.0% whereas the best descriptor in terms of local features for skin burn images was SIFT, with an average accuracy of 74.7%. It can be concluded that a combination of global and local features is able to provide sufficient information for the classification of the skin burn depths. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/19716/1/A%20comparative%20study%20of%20features%20extracted%20in%20the%20classification%20of%20human%20skin%20burn%20depth%20%28abstrak%29.pdf Kuan, Pei Nei and Li, Stephanie Chua Hui and Ehfa, binti Bujang Safawi (2017) A comparative study of features extracted in the classification of human skin burn depth. Journal of Telecommunication, Electronic and Computer Engineering, 9 (3-11). pp. 47-50. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041744263&partnerID=40&md5=8950af6f4c87c244a349678d97108128 |
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Q Science (General) R Medicine (General) Kuan, Pei Nei Li, Stephanie Chua Hui Ehfa, binti Bujang Safawi A comparative study of features extracted in the classification of human skin burn depth |
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The first burn treatment provided to patient is usually based on the first evaluation of the skin burn injury by determining the burn depths. In this paper, the objective is to conduct a comparative study of the different set of features extracted and used in the classification of different burn depths by using an image mining approach. Seven sets of global features and 5 local feature descriptors were studied on a skin burn dataset comprising skin burn images categorized into three burn classes by medical experts. The performance of the studied global and local features were evaluated using SMO, JRIP, and J48 on 10-fold cross validation method. The empirical results showed that the best set of features that was able to classify most of the burn depths consisted of mean of lightness, mean of hue, standard deviation of hue, standard deviation of A* component, standard deviation of B* component, and skewness of lightness with an average accuracy of 77.0% whereas the best descriptor in terms of local features for skin burn images was SIFT, with an average accuracy of 74.7%. It can be concluded that a combination of global and local features is able to provide sufficient information for the classification of the skin burn depths. |
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Article |
author |
Kuan, Pei Nei Li, Stephanie Chua Hui Ehfa, binti Bujang Safawi |
author_facet |
Kuan, Pei Nei Li, Stephanie Chua Hui Ehfa, binti Bujang Safawi |
author_sort |
Kuan, Pei Nei |
title |
A comparative study of features extracted in the classification of human skin burn depth |
title_short |
A comparative study of features extracted in the classification of human skin burn depth |
title_full |
A comparative study of features extracted in the classification of human skin burn depth |
title_fullStr |
A comparative study of features extracted in the classification of human skin burn depth |
title_full_unstemmed |
A comparative study of features extracted in the classification of human skin burn depth |
title_sort |
comparative study of features extracted in the classification of human skin burn depth |
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Universiti Teknikal Malaysia Melaka |
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2017 |
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http://ir.unimas.my/id/eprint/19716/1/A%20comparative%20study%20of%20features%20extracted%20in%20the%20classification%20of%20human%20skin%20burn%20depth%20%28abstrak%29.pdf http://ir.unimas.my/id/eprint/19716/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041744263&partnerID=40&md5=8950af6f4c87c244a349678d97108128 |
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