Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging
This work addresses the critical medical problem of accurately diagnosing cervical spine fractures in patients using CT imaging, which is incremental as it applies a hybrid deep learning method to a specific domain.
The paper tackled automated detection of cervical spine fractures in CT scans using a deep convolutional neural network with a bidirectional LSTM layer, achieving classification accuracies of 70.92% on a balanced test set and 79.18% on an imbalanced test set.
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.