Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
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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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottleneck Residual Block •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Dropout •
Fire Module •
Global Average Pooling •
Kaiming Initialization •
Max Pooling •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Softmax •
SqueezeNet •
Xavier Initialization
