Three stage cervical cancer classifier based on hybrid ensemble learning with modified binary PSO using pretrained neural networks

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.

KEYWORDS:

• Deep neural networks
• Pap-smear
• ensemble methods
• particle swarm optimization
• cervical cancer detection
• image classification
• support vector machine
• transfer learning

Acknowledgements

The authors thank MDE LAB to provide data sets available online. Also, thank the Office on Women’s Health, National Institute of Health, American Cancer Society, and International Agency for Research on Cancer and provide facts on cervical cancer which motivates authors to do research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Sanjay Kumar Singh is a Ph.D. research scholar at IK Gujral Punjab technical university, Jalandhar. He has received B. Tech and M. Tech degree from ABV-IIITM, Gwalior in field of Information Technology. He has published more than 20 research publication in international journals and reputed conferences. He is also member of IAENG, Indian science congress and other reputed member councils.

Dr. Anjali Goyal has received her Bachelor degree in Electronics in 1993 from Kurukshetra University and Master degree in Computer Applications in 1996 from Panjab University, Chandigarh. She has received her Ph.D degree from Punjab Technical University, Jalandhar, India in 2013. Presently she is working as Assistant Professor in Department of Computer Application at Guru Nanak Institute of Management and Technology, Ludhiana affiliated to PTU.