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.
计算机辅助诊断在宫颈癌筛查中常面临阴道镜图像分割不准确及边界检测不完整等挑战。本研究提出一种轻量化分割模型，旨在提升计算效率与分割精度。该架构集成双编码器主干网络（ResNet50与MobileNetV2），分别实现高层语义特征提取与高效特征捕获。通过轻量化空洞空间金字塔池化模块记录多尺度上下文信息，并引入新型注意力模块聚焦关键区域以增强特征细节。解码器采用先进上采样与特征融合技术优化分割边界。实验结果显示优异性能：训练准确率97.56%、验证准确率96.04%、特异性97.00%、敏感性96.78%、Dice系数98.71%、交并比97.56%，各项指标均优于现有方法。通过与海得拉巴MNJ肿瘤研究所区域中心合作，本研究证实该模型具备临床实际应用潜力，能实现精准可靠的阴道镜图像分割。此项研究推动了宫颈癌诊断工具向高效精准化发展，有助于优化诊疗流程并改善患者预后。
Attention-Enhanced Lightweight Architecture with Hybrid Loss for Colposcopic Image Segmentation
肿瘤(癌症)患者之家