Abnormal cervical cell detection based on an adaptive margin-based feature selection method
Lili Zhao, Kuan Li, Hongyun Yang, Jianping Yin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
In an abnormal cervical cell detection system the discriminated abilities of different features are not same so the optimized combination method of all features is an essential component to this system. Feature selection can improve each feature utilization ratio and the performance of the classification problem. The previous efforts of cervical abnormal cell detection are mainly focused on changing feature space into a new one by using a binary weight vector. In this work, the binary weight values are extended to the multiple weight values. According to the statistical distribution situation of the data, an adaptive margin-based weighted feature selection method is proposed in this paper. This method performs best compared with the other 3 methods. The experimental result achieves 96% accuracy in a real-world cervical smear image dataset.