Cervical Spine Fracture Detection and Classification Using Two-Stage Deep Learning Methodology

• Published in: IEEE access Vol. 12; pp. 72131 - 72142
• Main Authors: Yaseen, Muhammad, Ali, Maisam, Ali, Sikandar, Hussain, Ali, Joo, Moon-Il, Kim, Hee-Cheol
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Biomedical imaging; Bones; Cervical spine; Computed tomography; computed tomography (CT); Computer vision; Convolutional neural networks; Deep learning; Detection algorithms; Emergency medical services; Feature extraction; Fractures; Human error; Image enhancement; Machine learning; Medical diagnosis; Medical electronics; medical image; Medical imaging; Object recognition; Paralysis; Performance evaluation; Spine; Vertebrae; YOLO
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3398061

Cervical spine fractures are a medical emergency that can cause permanent paralysis and even death. Traditional fracture detection techniques, such as manual radiography image interpretation, are time-consuming and prone to human error. Deep learning algorithms have shown promising results in various medical imaging applications i.e., disease diagnosis, including fracture detection of bones. In this study, we propose a two-stage approach for detecting cervical spine fractures. The first stage employs a convolutional neural network (CNN) model to determine the presence or absence of a fracture in the cervical spine, using a dataset of cervical spine Computed Tomography (CT) scan images as well as Grad-CAM for enhanced visualization and interpretation. In the second stage, our focus shifts to specific vertebrae within the cervical spine. To accomplish this task, we trained and evaluated the performance of the YOLOv5 and YOLOv8 models with 9170 images consisting of seven vertebrae. The detection results of both YOLO versions are compared and evaluated. The precision, recall, mAP50, and mAP50-90 were 0.900, 0.890, 0.935, 0.872, respectively. The results of this research demonstrate the potential of deep learning-based approaches for cervical spine fracture detection. By automating the detection process, these algorithms can assist radiologists and healthcare professionals in making accurate and timely diagnoses, leading to improved patient outcomes.