Attention-Guided Fusion of EfficientNet-B0 and Swin Transformer for Cervical Cancer Classification

Abstract: The interpretation of colposcopy images is a critical yet subjective component of cervical cancer screening. To enhance this process, we propose a novel hybrid deep learning framework for the classification of cervical lesions. Our model integrates EfficientNet-B0, adept at extracting localized hierarchical features, with a Swin-Tiny Transformer, which excels at modeling long-range dependencies and global context. Moving beyond basic fusion techniques, we introduce a novel cross-attention fusion mechanism, augmented with channel and spatial attention modules. This design selectively highlights the most discriminative inter-feature relationships while maintaining computational efficiency. Evaluated on the International Agency for Research on Cancer (IARC) colposcopy image dataset, our framework achieves an accuracy of 94.76%, significantly outperforming a concatenation-based fusion model (83.99%). This represents an absolute improvement of 10.77 percentage points and captures 67.3% of the residual performance margin toward perfect ac-curacy. The model also demonstrates robust performance across other metrics, including a precision of 94.68%, recall of 94.82%, F1-score of 94.74%, and a Cohen’s Kappa of 89.48%. These results indicate that our approach can enhance both the accuracy and reliability of cervical cancer screening, offering valuable support for clinical decision-making.

Twisibile Mwalughali, Emmanuel C. OGU and Evason Karanja. “Attention-Guided Fusion of EfficientNet-B0 and Swin Transformer for Cervical Cancer Classification”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.01702101

@article{Mwalughali2026,
title = {Attention-Guided Fusion of EfficientNet-B0 and Swin Transformer for Cervical Cancer Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.01702101},
url = {http://dx.doi.org/10.14569/IJACSA.2026.01702101},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Twisibile Mwalughali and Emmanuel C. OGU and Evason Karanja}
}