CerviSegNet-DistillPlus: An Efficient Knowledge Distillation Model for Enhancing Early Detection of Cervical Cancer Pathology

The global challenge of cervical cancer calls for advancements in early detection and diagnosis. Our study introduces CerviSegNet-DistillPlus, a state-of-the-art deep-learning framework that elevates cervical cancer cell detection and segmentation. It leverages the DeepLabV3+ architecture, enhanced with leading-edge knowledge distillation and model pruning to efficiently process diverse data and operate within computational limits typical in clinical settings. This results in a compact yet highly accurate model that excels in computational efficiency. In a comparative analysis, CerviSegNet-DistillPlus achieves top performance, improving accuracy by 0.8%, 1.5%, and 2% over its nearest rivals on the Cx22, Technical University of Denmark/Herlev Hospital Pap Smear Database(DTU/HERLEV), and SIPaKMeD datasets, respectively. On the Cx22 dataset, it attains a sensitivity of 0.9623, specificity of 0.9219, accuracy of 0.94, and a top Dice coefficient of 0.9855. For the DTU/HERLEV dataset, CerviSegNet-DistillPlus demonstrates a sensitivity of 0.9617, specificity of 0.91, accuracy of 0.9365, and a remarkable Dice coefficient of 0.9892. Furthermore, on the SIPaKMeD dataset, it achieves a sensitivity of 0.9369, specificity of 0.899, accuracy of 0.9249, and an outstanding Dice coefficient of 0.9734. The integration of knowledge distillation and test-time augmentation significantly improves segmentation accuracy, while model pruning substantially reduces computational complexity, making it well-suited for efficient deployment in clinical settings. This innovative integration of advanced techniques achieves high accuracy and efficiency for cervical cancer cell detection. CerviSegNet-DistillPlus stands as a powerful, efficient, and accessible tool for early cervical cancer diagnosis, offering the potential to improve patient outcomes and make a significant contribution to the global fight against cervical cancer.