CervNet: A Novel Multimodal Neural Network to Diagnose Cervical Spondylosis from X-Ray Images
Year: 2025
Type: article
Abstract: Cervical spondylosis is a degenerative condition of the cervical spine that affects over 85% of individuals above 60 and nearly 25% under 40.Although X-rays are the most accessible imaging modality, manual interpretation achieves only 68.3% accuracy, often requiring costly CT or MRI scans for confirmation.To address this limitation, we developed CervNet, a multimodal deep learning model that integrates X-ray images with quantitative spinal parameters.Vertebrae C2-C7 were localized using a Single Shot Detector, and 77 clinically relevant spinal parameters were computed.An attention block extracted diagnostic patterns such as disc space narrowing, which were fused with EfficientNetB7-derived visual features including osteophytes and endplate sclerosis.CervNet achieved 99.09% accuracy, surpassing prior models.By mimicking real radiological workflows, CervNet improves diagnostic accuracy and interpretability, offering a practical solution for early detection of cervical spondylosis in resource-limited settings.
Authors: Srivatsav Kannan, Valliappan Raman, Kalaivani Krishna Kumar, Prabhavathy Mohanraj
Institution: Coimbatore Institute of Technology
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7087779559