利用深度卷积神经网络对赛马肢体X光片进行分类。

IF 1.3 Q2 VETERINARY SCIENCES
Veterinary Record Open Pub Date : 2023-01-29 eCollection Date: 2023-06-01 DOI:10.1002/vro2.55
Raniere Gaia Costa da Silva, Ambika Prasad Mishra, Christopher Michael Riggs, Michael Doube
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引用次数: 0

摘要

目的:评估深度卷积神经网络从一系列 48 幅赛马四肢标准视图中对解剖位置和投影进行分类的能力:10 家独立兽医诊所为兽医检查而制作的马匹四肢图像集中的 X 光片(N = 9504)被用于训练、验证和测试(分别为 116、40 和 42 张 X 光片)作为开源机器学习框架 PyTorch 一部分的六种深度学习架构。对准确率最高的深度学习架构的批量大小进行了进一步研究:六个深度学习架构的 Top-1 准确率在 0.737 到 0.841 之间。最佳深度学习架构(ResNet-34)的 Top-1 准确率介于 0.809 到 0.878 之间,具体取决于批量大小。ResNet-34(批量规模 = 8)达到了最高的 Top-1 准确率(0.878),大部分(91.8%)误分类是由于侧向误差造成的。类激活图显示,驱动模型决策的是关节形态,而不是侧标或其他非解剖图像区域:深度卷积神经网络可将马匹进口前的X光片分类为48个标准视图,包括中等程度的侧位分辨,与侧位标记的存在无关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Classification of racehorse limb radiographs using deep convolutional neural networks.

Classification of racehorse limb radiographs using deep convolutional neural networks.

Classification of racehorse limb radiographs using deep convolutional neural networks.

Classification of racehorse limb radiographs using deep convolutional neural networks.

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 0.809 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.

Conclusions: 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.

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来源期刊
Veterinary Record Open
Veterinary Record Open VETERINARY SCIENCES-
CiteScore
3.00
自引率
0.00%
发文量
25
审稿时长
19 weeks
期刊介绍: Veterinary Record Open is a journal dedicated to publishing specialist veterinary research across a range of topic areas including those of a more niche and specialist nature to that considered in the weekly Vet Record. Research from all disciplines of veterinary interest will be considered. It is an Open Access journal of the British Veterinary Association.
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