Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks

Forensic diatom test has been widely accepted as a way of providing supportive evidences in the diagnosis of drowning. The current workflow is primarily based on the observation of diatoms by forensic pathologists under a microscopy, and this process can be very time-consuming. In this paper, we dem...

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Bibliographic Details
Published in:International journal of legal medicine 2021-03, Vol.135 (2), p.497-508
Main Authors: Yu, Weimin, Xue, Ye, Knoops, Rob, Yu, Danyuan, Balmashnova, Evgeniya, Kang, Xiaodong, Falgari, Pietro, Zheng, Dongyun, Liu, Pengfei, Chen, Hui, Shi, He, Liu, Chao, Zhao, Jian
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Digital imaging
Forensic computing
Forensic Medicine
Forensic sciences
Machine learning
Medical Law
Medicine
Medicine & Public Health
Microscopy
Neural networks
Original Article
Performance enhancement
Searching
Workflow
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Summary:Forensic diatom test has been widely accepted as a way of providing supportive evidences in the diagnosis of drowning. The current workflow is primarily based on the observation of diatoms by forensic pathologists under a microscopy, and this process can be very time-consuming. In this paper, we demonstrate a deep learning-based approach for automatically searching diatoms in scanning electron microscopic images. Cross-validation studies were performed to evaluate the influence of magnification on performance. Moreover, various training strategies were tested to improve the performance of detection. The conclusion shows that our approach can satisfy the necessary requirements to be integrated as part of an automatic forensic diatom test.
ISSN:0937-9827
1437-1596
DOI:10.1007/s00414-020-02392-z