Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
Masuda Akter Tonima, Fatemeh Esfahani, Austin Dehart, Youmin Zhang
TL;DR
This paper presents a lightweight machine learning algorithm inspired by AlexNet, Inception, and SqueezeNet for automating the sorting of canine radiographs by view and anatomy, aiming to improve efficiency in veterinary imaging.
Contribution
A novel lightweight neural network model tailored for veterinary radiograph sorting, outperforming existing models in accuracy while being more efficient.
Findings
The proposed model is lighter than SqueezeNet.
It achieves higher accuracy than AlexNet, ResNet, DenseNet, and SqueezeNet.
The algorithm enhances automation in veterinary radiograph sorting.
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.
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Advanced Neural Network Applications
MethodsConcatenated Skip Connection · Fire Module · Dense Block · Average Pooling · 1x1 Convolution · Residual Block · Max Pooling · Softmax · Xavier Initialization · Kaiming Initialization