Identifying gross post-mortem organ images using a pre-trained convolutional neural network.
artificial intelligence
assisted diagnostics
autopsy
computer vision
convolutional neural network
deep learning
image recognition
post-mortem images
Journal
Journal of forensic sciences
ISSN: 1556-4029
Titre abrégé: J Forensic Sci
Pays: United States
ID NLM: 0375370
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
received:
31
08
2020
revised:
03
10
2020
accepted:
06
10
2020
pubmed:
27
10
2020
medline:
22
6
2021
entrez:
26
10
2020
Statut:
ppublish
Résumé
Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post-mortem gross images of visceral organs. This proof-of-concept study used 537 gross post-mortem images of dissected brain, heart, lung, liver, spleen, and kidney, which were randomly divided into a training and teaching datasets for the pre-trained CNN Xception. The CNN was trained using the training dataset and subsequently tested on the testing dataset. The overall accuracies were >95% percent for both training and testing datasets and have an F1 score of >0.95 for all dissected organs. This study showed that small datasets of post-mortem images can be classified with a very high accuracy using a pre-trained CNN. This novel area has the potential for future application in data mining, education and teaching, case review, research, quality assurance, auditing purposes, and identifying pathology.
Identifiants
pubmed: 33105027
doi: 10.1111/1556-4029.14608
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
630-635Informations de copyright
© 2020 American Academy of Forensic Sciences.
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