Classification of racehorse limb radiographs using deep convolutional neural networks.
Thoroughbred racehorse
deep learning
machine learning
radiograph
Journal
Veterinary record open
ISSN: 2052-6113
Titre abrégé: Vet Rec Open
Pays: United States
ID NLM: 101653671
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
25
05
2022
revised:
07
12
2022
accepted:
16
12
2022
entrez:
2
2
2023
pubmed:
3
2
2023
medline:
3
2
2023
Statut:
epublish
Résumé
To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Radiographs ( Top-1 accuracy of six deep learning architectures ranged from 0.737 to 0.841. Top-1 accuracy of the best deep learning architecture (ResNet-34) ranged from 0.809 to 0.878, depending on batch size. ResNet-34 (batch size = 8) achieved the highest top-1 accuracy (0.878) and the majority (91.8%) of misclassification was due to laterality error. Class activation maps indicated that joint morphology, not side markers or other non-anatomical image regions, drove the model decision. Deep convolutional neural networks can classify equine pre-import radiographs into the 48 standard views including moderate discrimination of laterality, independent of side marker presence.
Identifiants
pubmed: 36726400
doi: 10.1002/vro2.55
pii: VRO255
pmc: PMC9884469
doi:
Banques de données
figshare
['10.6084/m9.figshare.c.5921813']
Types de publication
Journal Article
Langues
eng
Pagination
e55Informations de copyright
© 2023 The Authors. Veterinary Record Open published by John Wiley & Sons Ltd on behalf of British Veterinary Association.
Déclaration de conflit d'intérêts
The authors declare they have no conflicts of interest.
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