Papers › Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

22 Feb 2021arXiv:2102.11385archive 2025-07-28

Masuda Akter Tonima, Fatemeh Esfahani, Austin Dehart, Youmin Zhang

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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Anatomy BIG-bench Machine Learning

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1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Fire Module Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Softmax SqueezeNet Xavier Initialization

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