Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

2021/02/22 by Masuda Akter Tonima, Tonima, Masuda Akter, Fatemeh Esfahani +5
Computer Science · Medicine · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2102.11385

openalex publication_date 2021/02/22 · openalex created_date 2021/03/01 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Gradient-based learning applied to document recognition
• Backpropagation Applied to Handwritten Zip Code Recognition
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Related

• RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
• VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
• Toward Robust Canine Cardiac Diagnosis: Deep Prototype Alignment Network-Based Few-Shot Segmentation in Veterinary Medicine
• Equine radiograph classification using deep convolutional neural networks
• Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012