  Fractures of the cervical spine are a medical emergency and may lead to
permanent paralysis and even death. Accurate diagnosis in patients with
suspected fractures by computed tomography (CT) is critical to patient
management. In this paper, we propose a deep convolutional neural network
(DCNN) with a bidirectional long-short term memory (BLSTM) layer for the
automated detection of cervical spine fractures in CT axial images. We used an
annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to
train and validate the model. The validation results show a classification
accuracy of 70.92% and 79.18% on the balanced (104 positive and 104 negative
cases) and imbalanced (104 positive and 419 negative cases) test datasets,
respectively.
