Cervical Spine Fracture Detection through Two-stage Approach of Mask Segmentation and Windowing based on Convolutional Neural Network

Neck pain may be caused by cervical bone fracture, which must be promptly detected and treated, as severe cases can lead to paralysis or even death. The diagnostic precision of radiologists in the identification of cervical spine fractures depends on the clinical manifestation of the patient. Current fracture detection accuracy among radiologists stands at only 73.98% for alert blunt traumatic patients. To address this concern, this paper presents an approach based on deep learning models that can quickly analyze CT scans and diagnose cervical spine fracture. The approach includes two stages: Stage 1 utilizes UNet-EfficientNet for CT image segmentation, while Stage 2 incorporates CrackNet-LSTM to achieve spinal injury detection. Notably, the models excel in accurately identifying fractures. Implementing these strategies with the aforementioned models yields impressive results: 99.91% accuracy for Stage 1, 94.9% accuracy for Stage 2, and a combined accuracy of 94.9% for the overall examination process. This approach significantly improves the accuracy and the efficiency, thus proving to be highly qualified in assisting radiologists and alleviating their workload in detecting cervical spine fractures.