A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis
- Title
- A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis
- Creator
- Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna
- Description
- This study presents a deep learning-based approach for the classification of cervical stenosis using MRI spine images, integrating multiple phases such as preprocessing, segmentation, feature extraction, and classification. A U-Net-based segmentation model effectively delineates key anatomical structures, including the spinal canal, intervertebral discs (IVDs), and neural foramen, improving feature extraction and classification accuracy. Furthermore, ResNet-50 is employed for feature map generation, leveraging deep hierarchical representations to extract meaningful spatial patterns from MRI slices. For classification, a Vision Transformer (ViT)-based model is utilized, taking advantage of its self-attention mechanism to capture both local and global dependencies within MRI images. Unlike conventional CNN-based models, ViT processes MRI scans as patches, enabling a more context-aware analysis of stenotic regions. The model is trained using an 80%20% train-test split and evaluated using standard performance metrics, achieving an accuracy of 92.60%, precision of 90.16%, recall of 95.43%, and an F1-score of 91.56%. These results indicate that the ViT model outperforms traditional CNN-based classifiers in cervical stenosis detection, ensuring higher sensitivity and specificity in real-world clinical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
- Source
- Lecture Notes in Networks and Systems;Volume;1772 LNNS;pp.243-253
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- artificial intelligence; Cervical stenosis; deep learning; MRI; spine degeneration
- Coverage
- Mukku L., CHRIST (Deemed to be University), Bangalore, India; Burri V., Colorado State University, Fort Collins, United States; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23673370; ISBN: 978-303214043-2;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mukku, Lalasa; Burri, Vikas; Lamani, Manjunath Ramanna, “A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25388.