CNN-ViT Hybrid for Pneumonia Detection: Theory and Empiric on Limited Data without Pretraining

This research explored the hybridization of CNN and ViT within a training dataset of limited size, and introduced a distinct class imbalance. The training was made from scratch with a mere focus on theoretically and experimentally exploring the architectural strengths of the proposed hybrid model. Experiments were conducted across varied data fractions with balanced and imbalanced training datasets. Comparatively, the hybrid model, complementing the strengths of CNN and ViT, achieved the highest recall of 0.9443 (50% data fraction in balanced) and consistency in F1 score around 0.85, suggesting reliability in diagnosis. Additionally, the model was successful in outperforming CNN and ViT in imbalanced datasets. Despite its complex architecture, it required comparable training time to the transformers in all data fractions.