A Website-Based Classification of Cervical Spine Conditions Using Convolutional Neural Network

Accurate assessment of cervical spine alignment is essential for diagnosing and monitoring cervical spine disorders. However, manual interpretation of cervical spine X-ray images is time-consuming and prone to variability. To address this, the study proposes an automated web-based prototype that uses a convolutional neural network (CNN) and a rule-based Cobb angle classification for cervical spinal conditions (lordosis, kyphosis, and straight neck). A stratified fivefold cross-validation (CV) framework with separate train, validation, and test splits per fold, incorporating transfer learning (TL), fine-tuning (FT), and hyperparameter optimisation (HPO) using Optuna, was employed. Two pre-trained CNNs, ResNet152V2 and ConvNeXt Tiny, were evaluated using the same data splits. Results showed that ResNet152V2 outperformed ConvNeXt Tiny, achieving a mean accuracy of 86.0 ± 1.21% (95% bootstrap confidence intervals [CI] 84.2%-87.9%), demonstrating a greater stability with a lower standard deviation (SD) and a narrower CI. Both the image-level paired permutation test and the McNemar test showed a statistically significant performance difference of 4.47%. Despite ResNet152V2's heavier architecture, higher GPU latency, and lower GPU throughput, the practical processing time difference is minor because the computation runs on a backend server rather than on a resource-constrained mobile platform. Therefore, the optimised ResNet152V2 model was selected for deployment via a Gradio web interface hosted on Hugging Face. In addition, the CNN-based classification significantly outperformed the Cobb angle-based classification (96.67% vs 88.33% accuracy). A 95% bootstrap CI with 10,000 resamples showed a significant improvement in CNN-based classification over the rule-based classification. A right-tailed test yielded a p-value of 0.0292 at the 0.05 significance level, consistent with 97.08% of bootstrap samples showing a positive difference in accuracy. Ultimately, this study demonstrates the feasibility of integrating an optimised deep learning model and Cobb-angle classification into a real-time cervical spine research prototype. Future work will focus on larger datasets and lightweight model designs to enhance its applicability.