Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map

Abstract

Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.