Identification of diatom taxonomy by a combination of region-based full convolutional network, online hard example mining, and shape priors of diatoms.
Diatom test
Drowning
Forensic science
Taxonomy identification
Journal
International journal of legal medicine
ISSN: 1437-1596
Titre abrégé: Int J Legal Med
Pays: Germany
ID NLM: 9101456
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
received:
24
02
2021
accepted:
05
07
2021
pubmed:
21
7
2021
medline:
9
11
2021
entrez:
20
7
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34282483
doi: 10.1007/s00414-021-02664-2
pii: 10.1007/s00414-021-02664-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2519-2530Subventions
Organisme : National Natural Science Foundation of China
ID : 61202267
Organisme : Zhejiang Province Public Welfare Technology Application Research Project
ID : LGG19F030009
Organisme : Guangzhou Science and Technology Program key projects
ID : 2019030001
Organisme : Guangzhou Science and Technology Program key projects
ID : 2019030012
Organisme : Ministry of Public Security of the People's Republic of China
ID : 2020GABJC38
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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