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Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy

Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the c...

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Autores principales: Gutiérrez-Fragoso, Karina, Acosta-Mesa, Héctor Gabriel, Cruz-Ramírez, Nicandro, Hernández-Jiménez, Rodolfo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5514345/
https://www.ncbi.nlm.nih.gov/pubmed/28744318
http://dx.doi.org/10.1155/2017/5989105
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author Gutiérrez-Fragoso, Karina
Acosta-Mesa, Héctor Gabriel
Cruz-Ramírez, Nicandro
Hernández-Jiménez, Rodolfo
author_facet Gutiérrez-Fragoso, Karina
Acosta-Mesa, Héctor Gabriel
Cruz-Ramírez, Nicandro
Hernández-Jiménez, Rodolfo
author_sort Gutiérrez-Fragoso, Karina
collection PubMed
description Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future.
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spelling pubmed-55143452017-07-25 Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy Gutiérrez-Fragoso, Karina Acosta-Mesa, Héctor Gabriel Cruz-Ramírez, Nicandro Hernández-Jiménez, Rodolfo Comput Math Methods Med Research Article Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future. Hindawi 2017 2017-07-04 /pmc/articles/PMC5514345/ /pubmed/28744318 http://dx.doi.org/10.1155/2017/5989105 Text en Copyright © 2017 Karina Gutiérrez-Fragoso 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
Gutiérrez-Fragoso, Karina
Acosta-Mesa, Héctor Gabriel
Cruz-Ramírez, Nicandro
Hernández-Jiménez, Rodolfo
Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title_full Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title_fullStr Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title_full_unstemmed Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title_short Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy
title_sort optimization of classification strategies of acetowhite temporal patterns towards improving diagnostic performance of colposcopy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5514345/
https://www.ncbi.nlm.nih.gov/pubmed/28744318
http://dx.doi.org/10.1155/2017/5989105
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