Cervical Cancer Single Cell Image Data Augmentation Using Residual Condition Generative Adversarial Networks
Siyu Chen, Dongrui Gao, Lutao Wang, Yongqing Zhang
Abstract
Early detection of cervical cancer is key to detecting and treating cancer. Applying the computer to the detection of cervical cancer can get more accurate results. However, data acquisition has become our main challenge. In order to solve the problem of insufficient data, this paper proposes a method based on residual network and generative adversarial network (RCGAN) for data augmentation of cervical single-cell images. At the same time, our experimental results are verified by a classification model. Experiments show that the method proposed in this paper can effectively expand the data set and improve the classification effect (accuracy rate is 95.18%, accuracy is 96.10%, recall rate is 98.50%, and F1-Score is 97.28%). Therefore, the work of this article is of great significance for the discovery and prevention of cervical cancer.