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In International journal of legal medicine

Diatom test is one of the commonly used diagnostic methods for drowning in forensic pathology, which provides supportive evidence for drowning. However, in forensic practice, it is time-consuming and laborious for forensic experts to classify and count diatoms, whereas artificial intelligence (AI) is superior to human experts in processing data and carrying out classification tasks. Some AI techniques have focused on searching diatoms and classifying diatoms. But, they either could not classify diatoms correctly or were time-consuming. Conventional detection deep network has been used to overcome these problems but failed to detect the occluded diatoms and the diatoms similar to the background heavily, which could lead to false positives or false negatives. In order to figure out the problems above, an improved region-based full convolutional network (R-FCN) with online hard example mining and the shape prior of diatoms was proposed. The online hard example mining (OHEM) was coupled with the R-FCN to boost the capacity of detecting the occluded diatoms and the diatoms similar to the background heavily and the priors of the shape of the common diatoms were explored and introduced to the anchor generation strategy of the region proposal network in the R-FCN to locate the diatoms precisely. The results showed that the proposed approach significantly outperforms several state-of-the-art methods and could detect the diatom precisely without missing the occluded diatoms and the diatoms similar to the background heavily. From the study, we could conclude that (1) the proposed model can locate the position and identify the genera of common diatoms more accurately; (2) this method can reduce the false positives or false negatives in forensic practice; and (3) it is a time-saving method and can be introduced.

Deng Jiehang, Guo Wenquan, Zhao Youwei, Liu Jingjian, Lai Runhao, Gu Guosheng, Zhang Yalong, Li Qi, Liu Chao, Zhao Jian

2021-Jul-20

Diatom test, Drowning, Forensic science, Taxonomy identification