Attention-enhanced deep learning for cervical cytology: combining convolutional networks with multi-head attention and fuzzy logic

“

Clinical Perspective The study highlights how AI, especially attention-based deep learning, can support cytologists by reducing manual workload, minimizing human error, and enabling early detection of cervical cancer. Technical Perspective By combining CNNs with attention mechanisms and fuzzy logic, the work advances the state of the art in medical image analysis, improving both feature extraction and interpretability in noisy cytology data. Research Perspective This approach opens new directions for explainable and robust AI in digital pathology, encouraging further exploration of hybrid deep learning frameworks in other cancer screening domains. Societal Perspective Improved automated cervical screening can expand access to timely cancer diagnosis, particularly in resource-limited settings, contributing to better women’s health outcomes worldwide.

”