Hybrid convolutional neural network-vision transformer architecture with parallel feature processing and cross-modal attention for lung segmentation | |
| Author | Pham Nguyen Thanh Khoa |
| Call Number | AIT PJPR PMDS no.25-07 |
| Subject(s) | Lungs--Diagnostic imaging--Data processing Image segmentation--Data processing Neural networks (Computer science) |
| Note | A project report submitted in partial fulfillment of the requirements for the Degree of Master of Science (Professional) in Data Science and Artificial Intelligence Applications |
| Publisher | Asian Institute of Technology |
| Abstract | Chest X-ray lung segmentation is essential for automated diagnosis of respiratory diseases, yet current approaches using either CNNs or Vision Transformers independently often is not fully captured all context of chest X-ray images . These images require both precise local boundary detection and global anatomical understanding due to overlapping structures and projection artifacts. While hybrid CNN-Transformer models have succeeded in computer vision, their application to medical image segmentation remains largely unexplored. This thesis presents hybrid architecture combining CNN and Transformer approaches for chest X-ray lung segmentation. The model employs parallel processing pathways where a CNN branch extracts local features and a Transformer branch captures global context. The key is a cross-modal attention mechanism enabling information exchange between branches. Through multi-head cross-attention, the CNN learns global context from the Transformer while the Transformer acquires spatial details from the CNN, creating enhanced representations that leverage both local precision and global coherence. The fused features are processed through a decoder with skip connections. Evaluation on standard datasets demonstrates improved segmentation performance compared to existing methods while maintaining computational efficiency. This research contributes the a hybrid CNN-Transformer architecture specifically designed for chest X-ray lung segmentation, establishing a foundation for future medical hybrid models. |
| Year | 2025 |
| Type | Project |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Professional Master in Data Science and Artificial Intelligence Applications (PMDS) |
| Chairperson(s) | Chutiporn Anutariya;Cherdasak Kingkan (Co-chairperson); |
| Examination Committee(s) | Chaklam Silpasuwanchai;Chantri Polprasert; |
| Degree | Master of Science (Professional) - Asian Institute of Technology, 2025 |