Cervical cancer is one of the most severe diseases threatening women's health. Early detection and diagnosis can significantly reduce cancer risk, in which cervical cytology classification is indispensable. Researchers have recently designed many networks for automated cervical cancer diagnosis, but the limited accuracy and bulky size of these individual models cannot meet practical application needs. To address this issue, we propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble. The samples misclassified by the ensemble model generate a new training set on which a linear classification model is trained as the meta-learner and performs the final predictions. In addition, a multi-level Stacking ensemble framework is designed to improve performance further. The method is evaluated on the SIPakMed, Herlev, and Mendeley datasets, achieving accuracies of 100\%, 100\%, and 100\%, respectively. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cervical cancer. The source code of the work is available at \underline{https://github.com/qianlinyi/Voting-Stacking-Ensemble}.

翻译：宫颈癌是威胁女性健康最严重的疾病之一。早期检测与诊断可显著降低癌症风险，其中宫颈细胞学分类至关重要。近年来，研究人员为自动化宫颈癌诊断设计了许多网络模型，但这些单一模型的精度有限且体积庞大，难以满足实际应用需求。为解决该问题，我们提出了一种投票堆叠集成策略：以三种Inception网络作为基学习器，通过投票集成整合其输出结果；被集成模型错误分类的样本构成新的训练集，在该训练集上训练的线性分类模型作为元学习器执行最终预测。此外，我们设计了一种多层次堆叠集成框架以进一步提升性能。该方法在SIPakMed、Herlev和Mendeley数据集上分别取得了100%、100%和100%的准确率，实验结果优于当前最先进方法，展现出减轻筛查工作量并辅助病理学家检测宫颈癌的潜力。本工作的源代码已公开可查于\underline{https://github.com/qianlinyi/Voting-Stacking-Ensemble}。