Computer Model for Detecting Cervical Spine Fractures based on Computed Tomography Images
DOI:
https://doi.org/10.47839/ijc.23.1.3435Keywords:
neural network, machine learning, convolutional neural network, pattern recognitionAbstract
The paper discusses methods of detecting cervical spine fractures based on computed tomography images using machine learning algorithms. Solving such a problem is important in the context of providing emergency care to patients with suspected spinal fractures, when accurate decision-making based on radiological data can be critical. In this case, such a machine learning model can speed up the work of a radiologist and reduce the importance of the human factor in making important decisions. After a review of analogs from the literature, it has been found that convolutional neural networks appear to be the most promising method. Using a publicly available dataset, a model "Fracture detection 3" based on a convolutional neural network is developed to solve the problem. The model demonstrates a classification accuracy of 98.25%, sensitivity of 99%, and specificity of 97.5%, which is ahead of the literature. For comparison with traditional methods, models based on the support vector method, decision tree, and k-nearest method are developed using a similar dataset. "Fracture detection 3" outperforms all developed models based on traditional methods in terms of classification accuracy.
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