Attention-Enhanced Lightweight Architecture with Hybrid Loss for Colposcopic Image Segmentation

Cervical cancer screening through computer-aided diagnosis often faces challenges like inaccurate segmentation and incomplete boundary detection in colposcopic images. This study proposes a lightweight segmentation model to improve accuracy and computational efficiency. The architecture integrates dual encoder backbones (ResNet50 and MobileNetV2) for high-level and efficient feature extraction. While a lightweight atrous spatial pyramid pooling (ASPP) module records multi-scale contextual information, a novel attention module improves feature details by concentrating on relevant locations. The decoder employs advanced upsampling and feature fusion for refined segmentation boundaries. The experimental results show exceptional performance: training accuracy of 97.56%, validation accuracy of 96.04%, 97.00% specificity, 96.78% sensitivity, 98.71% Dice coefficient, and 97.56% IoU, outperforming the existing methods. In collaboration with the MNJ Institute of Oncology Regional Center, Hyderabad, this work demonstrates potential for real-world clinical applications, delivering precise and reliable colposcopic image segmentation. This research advances efficient, accurate tools for cervical cancer diagnosis, improving diagnostic workflows and patient outcomes.