Cervical Spine Fracture Detection Using Deep Learning

Abstract

We've been looking into different disease detection models that use deep learning algorithms to look at medical radiograph images. Most of the studies have been done on lumbar spine diseases, but most of the studies were done on cervical spine diseases. Cervical radiculopathy is usually diagnosed based on MRI data, but it can be hard to diagnose even with an X-ray. MRI tests are expensive, so it can be hard for patients to get the right diagnosis. Cervical spine fractures are a medical emergency, and they can lead to permanent paralysis or even death. To make sure patients get the right diagnosis, it's important to use CT scans to check for fractures. We've come up with a Convolutional Neural Network (CNN) and Transfer Learning Model that can automatically detect cervical spine fractures from CT axial images, and we've trained and validated it using 4000 CT scans. We can even fine tune the transfer learning models to make it even more accurate. Keywords Convolutional Neural Networks, Computed tomography ,Deep learning , CT image , x ray , medical image , Spine disease , cervical spine fracture , Graphical user interface , Cervical radiculopathy.