Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging

TL;DR

This paper introduces a deep learning model combining CNN and BLSTM layers to automatically detect cervical spine fractures in CT scans, achieving around 70-79% accuracy on test datasets.

Contribution

The study presents a novel deep neural network architecture specifically designed for cervical spine fracture detection in CT images, with validation on a sizable annotated dataset.

Findings

Achieved 70.92% accuracy on balanced test set.

Achieved 79.18% accuracy on imbalanced test set.

Demonstrated potential for automated fracture detection in clinical settings.

Abstract

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.