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.
@article{arxiv.2010.13336,
title = {Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging},
author = {Hojjat Salehinejad and Edward Ho and Hui-Ming Lin and Priscila Crivellaro and Oleksandra Samorodova and Monica Tafur Arciniegas and Zamir Merali and Suradech Suthiphosuwan and Aditya Bharatha and Kristen Yeom and Muhammad Mamdani and Jefferson Wilson and Errol Colak},
journal= {arXiv preprint arXiv:2010.13336},
year = {2021}
}
Comments
This paper is accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 2021