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An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test
The diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test p...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437702/ https://www.ncbi.nlm.nih.gov/pubmed/36060761 http://dx.doi.org/10.3389/fmicb.2022.963059 |
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author | Yu, Weimin Xiang, Qingqing Hu, Yingchao Du, Yukun Kang, Xiaodong Zheng, Dongyun Shi, He Xu, Quyi Li, Zhigang Niu, Yong Liu, Chao Zhao, Jian |
author_facet | Yu, Weimin Xiang, Qingqing Hu, Yingchao Du, Yukun Kang, Xiaodong Zheng, Dongyun Shi, He Xu, Quyi Li, Zhigang Niu, Yong Liu, Chao Zhao, Jian |
author_sort | Yu, Weimin |
collection | PubMed |
description | The diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test process automatic. In this article, we propose an artificial intelligence solution based on the YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution in different scenarios, we collected five lab-grown diatom genera and samples of some organic tissues from drowning cases to investigate the potential upper/lower limits of the capability in detecting the diatoms and recognizing their genera. Based on the study of the article, a recall score of 0.95 together with the corresponding precision score of 0.9 were achieved on the samples of the five lab-grown diatom genera via cross-validation, and the accuracy of the evaluation in the cases of kidney and liver is above 0.85 based on the precision and recall scores, which demonstrate the effectiveness of the AI solution to be used in drowning forensic routine. |
format | Online Article Text |
id | pubmed-9437702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94377022022-09-03 An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test Yu, Weimin Xiang, Qingqing Hu, Yingchao Du, Yukun Kang, Xiaodong Zheng, Dongyun Shi, He Xu, Quyi Li, Zhigang Niu, Yong Liu, Chao Zhao, Jian Front Microbiol Microbiology The diatom test is a forensic technique that can provide supportive evidence in the diagnosis of drowning but requires the laborious observation and counting of diatoms using a microscopy with too much effort, and therefore it is promising to introduce artificial intelligence (AI) to make the test process automatic. In this article, we propose an artificial intelligence solution based on the YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution in different scenarios, we collected five lab-grown diatom genera and samples of some organic tissues from drowning cases to investigate the potential upper/lower limits of the capability in detecting the diatoms and recognizing their genera. Based on the study of the article, a recall score of 0.95 together with the corresponding precision score of 0.9 were achieved on the samples of the five lab-grown diatom genera via cross-validation, and the accuracy of the evaluation in the cases of kidney and liver is above 0.85 based on the precision and recall scores, which demonstrate the effectiveness of the AI solution to be used in drowning forensic routine. Frontiers Media S.A. 2022-08-19 /pmc/articles/PMC9437702/ /pubmed/36060761 http://dx.doi.org/10.3389/fmicb.2022.963059 Text en Copyright © 2022 Yu, Xiang, Hu, Du, Kang, Zheng, Shi, Xu, Li, Niu, Liu and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Yu, Weimin Xiang, Qingqing Hu, Yingchao Du, Yukun Kang, Xiaodong Zheng, Dongyun Shi, He Xu, Quyi Li, Zhigang Niu, Yong Liu, Chao Zhao, Jian An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title | An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title_full | An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title_fullStr | An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title_full_unstemmed | An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title_short | An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
title_sort | improved automated diatom detection method based on yolov5 framework and its preliminary study for taxonomy recognition in the forensic diatom test |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437702/ https://www.ncbi.nlm.nih.gov/pubmed/36060761 http://dx.doi.org/10.3389/fmicb.2022.963059 |
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