Attention-Guided Hybrid Network for Cervical Cancer Classification
Abdalla Ibrahim Abdalla Musa, Mahir Mohammed Sharif Adam · Ingénierie des systèmes d information · 2025
Cervical cancer remains a significant global health burden, necessitating accurate and efficient diagnostic tools.This paper proposes a novel deep learning architecture, the Squeeze-and-Excitation Attention-Guided Hybrid Network (SE-AG-HN), for the classification of cervical cancer from Pap smear images.The proposed method effectively addresses the challenges posed by image variability and subtle abnormalities by integrating Squeeze-and-Excitation (SE) attention and a hybrid convolutional neural network (CNN)recurrent neural network (RNN) structure.The SE attention module recalibrates feature channels to enhance discriminative information, while the hybrid architecture leverages both local and global contextual features.Experimental results on a benchmark cervical cancer dataset demonstrate the superior performance of SE-AG-HN compared to state-ofthe-art methods, highlighting its potential as a valuable tool for cervical cancer screening and diagnosis.