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

arXiv: https://arxiv.org/abs/2010.13336 · HTML: https://mlsanity.com/paper/2010.13336/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [S-Rocket: Selective Random Convolution Kernels for Time Series Classification](https://mlsanity.com/paper/2203.03445/) (2022)
- [ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans](https://mlsanity.com/paper/2105.12810/) (2021)
- [Deep Learning on Hester Davis Scores for Inpatient Fall Prediction](https://mlsanity.com/paper/2501.06432/) (2025)
- [Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods](https://mlsanity.com/paper/2311.05708/) (2023)
- [Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification](https://mlsanity.com/paper/2601.15235/) (2026)

## Abstract (arXiv)

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.