Hybrid CNN–transformer architecture for content based medical image retrieval
A hybrid Neural Network-Transformer encoder trained on four complimentary datasets maintains competitive accuracy across all four datasets while producing more retrieval-relevant embeddings and much more consistent performance under degraded queries suggests that the model may serve as a promising foundation for future CBMIR research and potential clinical exploration.