A Real-Time Deep Learning Approach for Classifying Cervical Spine Fractures

Abstract

The first seven vertebrae of our spine are called the cervical spine. It supports the weight of our head, encloses and safeguards our spinal cord, and permits a variety of head motions. The seven cervical vertebrae are joined at the rear of the bone by a kind of joint known as a facet joint. These joints enable us to move our necks forward, backward, and twist. Fractures of the cervical spine are a medical emergency that may lead to lifelong paralysis or even death. If left untreated and undetected, these fractures can worsen over time. Using computed tomog- raphy, a cervical spine fracture in individuals can be accurately diagnosed. Given the scarcity of research on the practical use of deep learning methods in detecting spine fractures in persons, it is imperative to address this gap. This study uses a dataset containing fracture and normal cervical spine computed tomography images. This study proposed modified transfer-learning-based MobileNetV2, InceptionV3, and Resnet50V2 models. An ablation study was also conducted to determine the optimal custom layers for models and data augmentation techniques. In addition, evaluation metrics have been used to analyze and compare the model’s performance. Among all the approaches, MobileNetV2 with augmentation has achieved the highest accuracy of 99.75%. Furthermore, the best-performing model has been deployed in a smartphone-based Android application