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 (Frontiers in Microbiology, 2022)
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 paper, we propose an artificial intelligence solution based on YOLOv5 framework for the automatic detection and recognition of the diatom genera. To evaluate the performance of this AI solution on different scenarios, we collected five lab-grown diatom genera and the 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 paper, a recall score 0.95 together with the corresponding precision score 0.9 were achieved on the samples of the five lab-grown diatom genera via cross validation, and the accuracy of the evaluation on 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.