Three stage cervical cancer classifier based on hybrid ensemble learning with modified binary PSO using pretrained neural networks,The Imaging Science Journal

当前位置： X-MOL 学术 › Imaging Sci. J. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Three stage cervical cancer classifier based on hybrid ensemble learning with modified binary PSO using pretrained neural networks
The Imaging Science Journal ( IF 1.1 ) Pub Date : 2020-01-02 , DOI: 10.1080/13682199.2020.1734306
Sanjay Kumar Singh ₁ , Anjali Goyal ₂

Affiliation

ABSTRACT Cervical cancer is one of the major challenges in developing nations like India.In recent years, a lot of research has been done todetect cervical cancer at an early stage through the pap-smear test, human papillomavirus test (HPV), etc. In this study, we have proposed athree-stage cervical cancer classifier to classify cervical cells among normal and abnormal cells using a hybrid ensemble classifier based onfeatures extracted using pre-trained neural networks. Furthermore, this work extends to classify the cells among different levels of dysplastic mainly mild, moderate and severe. The accuracy achieved for 2-class classification among normal and abnormal cells is up to 100% while for 4-class classification among normal, mild, moderate and severe dysplastic cells is up to 98.91% and 99.12% for new and old Herlev university hospital datasets respectively.

更新日期：2020-01-02

点击分享查看原文

点击收藏

阅读更多本刊最新论文