A Novel Approach to Cervical Spine Fracture Detection: Improving Diagnosis and Treatment

Abstract— The categorization of the cervical spine is essential for identifying and treating a variety of neurological diseases. In this study, we suggest a deep learning-based method for classifying cervical spine photos using convolutional neural networks (CNN) to detect whether a person has anomalies in the spine. Our approach intends to offer a non-invasive and effective method for early detection and diagnosis using cervical spine scans as input. In order to extract significant information from the photos of the cervical spine, the study uses a CNN architecture. The collection includes both normal and pathological examples of a wide variety of cervical spine pictures. To improve the important features and lessen noise, the photos are pre-processed. The CNN model is then trained using a sizable dataset to discover patterns of discrimination and establish a robust detection framework. Keywords—Cervical spine detection, Convolutional Neural Networks, CNN, deep learning, medical image analysis, diagnosis, detection accuracy, neurological conditions.