1. Deep Sequential Learning For Cervical Spine Fracture Detection On Computed Tomography Imaging
- Author
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Zamir Merali, Hojjat Salehinejad, Monica Tafur Arciniegas, Errol Colak, Jefferson R. Wilson, Priscila Crivellaro, Suradech Suthiphosuwan, Hui-Ming Lin, Oleksandra Samorodova, Edward Ho, Muhammad Mamdani, Kristen W. Yeom, and Aditya Bharatha
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,medicine.medical_specialty ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Computed tomography ,Convolutional neural network ,Machine Learning (cs.LG) ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Cervical spine fracture ,0302 clinical medicine ,FOS: Electrical engineering, electronic engineering, information engineering ,Paralysis ,medicine ,In patient ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Image and Video Processing (eess.IV) ,Electrical Engineering and Systems Science - Image and Video Processing ,Cervical spine ,030220 oncology & carcinogenesis ,Radiology ,Sequence learning ,medicine.symptom ,business - 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., This paper is accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI) 2021
- Published
- 2021
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