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Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System
We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889791/ https://www.ncbi.nlm.nih.gov/pubmed/27298758 http://dx.doi.org/10.1155/2016/9535027 |
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author | Su, Jie Xu, Xuan He, Yongjun Song, Jinming |
author_facet | Su, Jie Xu, Xuan He, Yongjun Song, Jinming |
author_sort | Su, Jie |
collection | PubMed |
description | We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review. |
format | Online Article Text |
id | pubmed-4889791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-48897912016-06-13 Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System Su, Jie Xu, Xuan He, Yongjun Song, Jinming Anal Cell Pathol (Amst) Research Article We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review. Hindawi Publishing Corporation 2016 2016-05-19 /pmc/articles/PMC4889791/ /pubmed/27298758 http://dx.doi.org/10.1155/2016/9535027 Text en Copyright © 2016 Jie Su et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Su, Jie Xu, Xuan He, Yongjun Song, Jinming Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title | Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title_full | Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title_fullStr | Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title_full_unstemmed | Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title_short | Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System |
title_sort | automatic detection of cervical cancer cells by a two-level cascade classification system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4889791/ https://www.ncbi.nlm.nih.gov/pubmed/27298758 http://dx.doi.org/10.1155/2016/9535027 |
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