Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN

Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic...

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Autores principales: Zhang, Jing, Wang, Xiangzhou, Ni, Guangming, Liu, Juanxiu, Hao, Ruqian, Liu, Lin, Liu, Yong, Du, Xiaohui, Xu, Fan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121882/
https://www.ncbi.nlm.nih.gov/pubmed/33990662
http://dx.doi.org/10.1038/s41598-021-89863-4
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author Zhang, Jing
Wang, Xiangzhou
Ni, Guangming
Liu, Juanxiu
Hao, Ruqian
Liu, Lin
Liu, Yong
Du, Xiaohui
Xu, Fan
author_facet Zhang, Jing
Wang, Xiangzhou
Ni, Guangming
Liu, Juanxiu
Hao, Ruqian
Liu, Lin
Liu, Yong
Du, Xiaohui
Xu, Fan
author_sort Zhang, Jing
collection PubMed
description Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.
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spelling pubmed-81218822021-05-17 Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN Zhang, Jing Wang, Xiangzhou Ni, Guangming Liu, Juanxiu Hao, Ruqian Liu, Lin Liu, Yong Du, Xiaohui Xu, Fan Sci Rep Article Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand. Nature Publishing Group UK 2021-05-14 /pmc/articles/PMC8121882/ /pubmed/33990662 http://dx.doi.org/10.1038/s41598-021-89863-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhang, Jing
Wang, Xiangzhou
Ni, Guangming
Liu, Juanxiu
Hao, Ruqian
Liu, Lin
Liu, Yong
Du, Xiaohui
Xu, Fan
Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title_full Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title_fullStr Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title_full_unstemmed Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title_short Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN
title_sort fast and accurate automated recognition of the dominant cells from fecal images based on faster r-cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121882/
https://www.ncbi.nlm.nih.gov/pubmed/33990662
http://dx.doi.org/10.1038/s41598-021-89863-4
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