Citation

Yu W, Xue Y, Knoops R, Yu D, Balmashnova E, Kang X, Falgari P, Zheng D, Liu P, Chen H, Shi H, Liu C, Zhao J. Int. J. Legal Med. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00414-020-02392-z

PMID

32789676

Abstract

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.

Language: en

Keywords

Artificial intelligence; Diatom test; Forensic science; Object detection; Scanning electron microscopy