Classification of racehorse limb radiographs using deep convolutional neural networks.

ABSTRACT:

Purpose

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.

Materials and methods

Radiographs (N = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validate and test (116, 40 and 42 radiographs, respectively) six deep learning architectures available as part of the open source machine learning framework PyTorch. The deep learning architectures with the best top-1 accuracy had the batch size further investigated.

Results

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

SUBMITTER: Costa da Silva RG 

PROVIDER: S-EPMC9884469 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature