{Reference Type}: Journal Article
{Title}: Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.
{Author}: Xie Y;Nie Y;Lundgren J;Yang M;Zhang Y;Chen Z;
{Journal}: Sensors (Basel)
{Volume}: 24
{Issue}: 11
{Year}: 2024 May 26
{Factor}: 3.847
{DOI}: 10.3390/s24113428
{Abstract}: The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.