# Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

arXiv: https://arxiv.org/abs/2102.11385 · HTML: https://mlsanity.com/paper/2102.11385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)

## Built on by (most cited first, 2 of 2)

- [Auto-Detection of Tibial Plateau Angle in Canine Radiographs Using a Deep Learning Approach](https://mlsanity.com/paper/2102.12544/) (2021)
- [Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs](https://mlsanity.com/paper/2302.12923/) (2023)

## Abstract (arXiv)

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.