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An Automatic Classification Method on Chronic Venous Insufficiency Images
Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors’ interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298992/ https://www.ncbi.nlm.nih.gov/pubmed/30560945 http://dx.doi.org/10.1038/s41598-018-36284-5 |
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author | Shi, Qiang Chen, Weiya Pan, Ye Yin, Shan Fu, Yan Mei, Jiacai Xue, Zhidong |
author_facet | Shi, Qiang Chen, Weiya Pan, Ye Yin, Shan Fu, Yan Mei, Jiacai Xue, Zhidong |
author_sort | Shi, Qiang |
collection | PubMed |
description | Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors’ interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI’s severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients’ medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors’ diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively. |
format | Online Article Text |
id | pubmed-6298992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62989922018-12-26 An Automatic Classification Method on Chronic Venous Insufficiency Images Shi, Qiang Chen, Weiya Pan, Ye Yin, Shan Fu, Yan Mei, Jiacai Xue, Zhidong Sci Rep Article Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors’ interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI’s severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients’ medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors’ diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively. Nature Publishing Group UK 2018-12-18 /pmc/articles/PMC6298992/ /pubmed/30560945 http://dx.doi.org/10.1038/s41598-018-36284-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shi, Qiang Chen, Weiya Pan, Ye Yin, Shan Fu, Yan Mei, Jiacai Xue, Zhidong An Automatic Classification Method on Chronic Venous Insufficiency Images |
title | An Automatic Classification Method on Chronic Venous Insufficiency Images |
title_full | An Automatic Classification Method on Chronic Venous Insufficiency Images |
title_fullStr | An Automatic Classification Method on Chronic Venous Insufficiency Images |
title_full_unstemmed | An Automatic Classification Method on Chronic Venous Insufficiency Images |
title_short | An Automatic Classification Method on Chronic Venous Insufficiency Images |
title_sort | automatic classification method on chronic venous insufficiency images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298992/ https://www.ncbi.nlm.nih.gov/pubmed/30560945 http://dx.doi.org/10.1038/s41598-018-36284-5 |
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