Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image
Port wine stain (PWS) is the benign congenital capillary malformation of skin, occurring in 0.3% to 0.5% of the population. In this paper, we build two automated support vector machine (SVM) based classifiers by extracting quantitative features from normal and PWS tissue images...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/8896069/ |
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| author | Shengnan Ai Chengming Wang Wenxin Zhang Wenchao Liao Juicheng Hsieh Zhenyu Chen Bin He Xiao Zhang Ning Zhang Ying Gu Ping Xue |
| author_facet | Shengnan Ai Chengming Wang Wenxin Zhang Wenchao Liao Juicheng Hsieh Zhenyu Chen Bin He Xiao Zhang Ning Zhang Ying Gu Ping Xue |
| author_sort | Shengnan Ai |
| collection | DOAJ |
| description | Port wine stain (PWS) is the benign congenital capillary malformation of skin, occurring in 0.3% to 0.5% of the population. In this paper, we build two automated support vector machine (SVM) based classifiers by extracting quantitative features from normal and PWS tissue images recorded by optical coherence tomography (OCT). We use both full feature set and simplified feature set for training. Accuracy of 92.7%, sensitivity of 92.3% and specificity of 93.8% were obtained for classifier with full feature set. Accuracy of 92.7%, sensitivity of 94.9% and specificity of 87.5% were obtained for classifier with simplified feature set. Our results suggest that extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for accurately and automatically identifying PWS margins during laser therapy. |
| format | Article |
| id | doaj-art-c90cf3e40ca9415683c636bfdd921695 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-c90cf3e40ca9415683c636bfdd9216952025-08-20T03:32:54ZengIEEEIEEE Photonics Journal1943-06552019-01-0111611110.1109/JPHOT.2019.29529038896069Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography ImageShengnan Ai0https://orcid.org/0000-0002-5070-3906Chengming Wang1Wenxin Zhang2https://orcid.org/0000-0003-0568-7369Wenchao Liao3Juicheng Hsieh4Zhenyu Chen5Bin He6Xiao Zhang7Ning Zhang8Ying Gu9Ping Xue10https://orcid.org/0000-0002-2210-7402State Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaSchool of Life Science, Beijing Institute of Technology, Beijing, ChinaInstitute of Forensic Science, Ministry of Public Security, Beijing, ChinaDepartment of Laser Medicine, Chinese PLA General Hospital, Beijing, ChinaState Key Laboratory of Low-Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, ChinaPort wine stain (PWS) is the benign congenital capillary malformation of skin, occurring in 0.3% to 0.5% of the population. In this paper, we build two automated support vector machine (SVM) based classifiers by extracting quantitative features from normal and PWS tissue images recorded by optical coherence tomography (OCT). We use both full feature set and simplified feature set for training. Accuracy of 92.7%, sensitivity of 92.3% and specificity of 93.8% were obtained for classifier with full feature set. Accuracy of 92.7%, sensitivity of 94.9% and specificity of 87.5% were obtained for classifier with simplified feature set. Our results suggest that extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for accurately and automatically identifying PWS margins during laser therapy.https://ieeexplore.ieee.org/document/8896069/Port wine stainoptical coherence tomographymachine learning. |
| spellingShingle | Shengnan Ai Chengming Wang Wenxin Zhang Wenchao Liao Juicheng Hsieh Zhenyu Chen Bin He Xiao Zhang Ning Zhang Ying Gu Ping Xue Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image IEEE Photonics Journal Port wine stain optical coherence tomography machine learning. |
| title | Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image |
| title_full | Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image |
| title_fullStr | Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image |
| title_full_unstemmed | Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image |
| title_short | Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image |
| title_sort | machine learning classification of port wine stain with quantitative features of optical coherence tomography image |
| topic | Port wine stain optical coherence tomography machine learning. |
| url | https://ieeexplore.ieee.org/document/8896069/ |
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