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

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.

@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},
  volume    = {17},
  number    = {2},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Twisibile Mwalughali and Emmanuel C. OGU and Evason Karanja},
  doi       = {10.14569/IJACSA.2026.01702101},
  url       = {https://doi.org/10.14569/IJACSA.2026.01702101}
}