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Automatic classification of cervical cancer from cytological images by using convolutional neural network
Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, w...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6259017/ https://www.ncbi.nlm.nih.gov/pubmed/30341239 http://dx.doi.org/10.1042/BSR20181769 |
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author | Wu, Miao Yan, Chuanbo Liu, Huiqiang Liu, Qian Yin, Yi |
author_facet | Wu, Miao Yan, Chuanbo Liu, Huiqiang Liu, Qian Yin, Yi |
author_sort | Wu, Miao |
collection | PubMed |
description | Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples ttest indicated that two models’ classification accuracy has a significant difference (P<0.05). The developed scheme we proposed was useful for classifying CCs from cytological images and the model can be served as a pathologist assistance to improve the doctor’s diagnostic level of CC, which has a great meaning and huge potential application in poor medical condition areas in China. |
format | Online Article Text |
id | pubmed-6259017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62590172018-12-11 Automatic classification of cervical cancer from cytological images by using convolutional neural network Wu, Miao Yan, Chuanbo Liu, Huiqiang Liu, Qian Yin, Yi Biosci Rep Research Articles Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples ttest indicated that two models’ classification accuracy has a significant difference (P<0.05). The developed scheme we proposed was useful for classifying CCs from cytological images and the model can be served as a pathologist assistance to improve the doctor’s diagnostic level of CC, which has a great meaning and huge potential application in poor medical condition areas in China. Portland Press Ltd. 2018-11-28 /pmc/articles/PMC6259017/ /pubmed/30341239 http://dx.doi.org/10.1042/BSR20181769 Text en © 2018 The Author(s). http://creativecommons.org/licenses/by/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Articles Wu, Miao Yan, Chuanbo Liu, Huiqiang Liu, Qian Yin, Yi Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title | Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title_full | Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title_fullStr | Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title_full_unstemmed | Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title_short | Automatic classification of cervical cancer from cytological images by using convolutional neural network |
title_sort | automatic classification of cervical cancer from cytological images by using convolutional neural network |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6259017/ https://www.ncbi.nlm.nih.gov/pubmed/30341239 http://dx.doi.org/10.1042/BSR20181769 |
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