Affiliations: [a] School of Computer Science, Northwestern Polytechnical University, Xi’an, P. R. China | [b] School of Software & Microelectronics, Northwestern Polytechnical University, Xi’an, P. R. China | [c] School of Computer Science and Technology, Xidian University, Xi an 710071, P. R. China
Correspondence:
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Corresponding author. Gang Sha, School of Computer Science, Northwestern Polytechnical University, Xi’an,
710072, P. R. China. E-mail: [email protected].
Abstract: Purpose:at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods:in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results:the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion:the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection.
Keywords: Deep learning, Yolov3-tiny, shrank model, fire module, detection and location
