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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1783692475965112320 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8121882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhangjing fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT wangxiangzhou fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT niguangming fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT liujuanxiu fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT haoruqian fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT liulin fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT liuyong fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT duxiaohui fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn AT xufan fastandaccurateautomatedrecognitionofthedominantcellsfromfecalimagesbasedonfasterrcnn |