A novel ensemble framework based on CNN models and Swin transformer for cervical cytology image classification
Yangfan Liu, Zifeng Wang, Anjun Dai, Wenhao Gu · 2024
Cervical cancer is one of the leading causes of women’s death. Currently, Pap smear images testing, one of the most conventional ways to check for cervical cancer, has a misidentification rate of around 40 percent which poses serious risks. Existing approaches to sorting Pap smear images are still not at an accurate enough level to be put into practical use. In this paper, we create an ensemble network by combining three CNN networks, namely DenseNet-169, VGG-19, and Xception with a Swin transformer to perform cervical cytolopy image classification on the standardized SIPaKMeD dataset and Mendeley LBC dataset. The proposed framework obtains an accuracy of 95.50% on the SIPaKMeD dataset and 98.65% on the Mendeley LBC dataset, which outperforms a majority of methods proposed on cervical cytology classification.