轻量级组合机器学习算法在犬躯干X光片分类中的应用
Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
摘要：
与人类医学领域取得的巨大技术进步相比，兽医领域缺乏自动化。实施机器学习技术可以缩短自动化过程的任何步骤。本文探讨了这些核心概念，并开始自动化排序射线照片的犬科动物的看法和解剖。这是通过开发一种新的轻量级算法来实现的，该算法的灵感来自AlexNet，Inception和SqueezeNet。事实证明，该模块比SqueezeNet更轻，同时保持比AlexNet，ResNet，DenseNet和SqueezeNet更高的准确性。
英文摘要：
The veterinary field lacks automation in contrast to the tremendous technological advances made in the human medical field. Implementation of machine learning technology can shorten any step of the automation process. This paper explores these core concepts and starts with automation in sorting radiographs for canines by view and anatomy. This is achieved by developing a new lightweight algorithm inspired by AlexNet, Inception, and SqueezeNet. The proposed module proves to be lighter than SqueezeNet while maintaining accuracy higher than that of AlexNet, ResNet, DenseNet, and SqueezeNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
