Three stage cervical cancer classifier based on hybrid ensemble learning with modified binary PSO using pretrained neural networks |
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Authors: | Sanjay Kumar Singh Anjali Goyal |
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Affiliation: | 1. I. K. Gujral Punjab Technical University, Jalandhar, Indiasanjayksingh.012@gmail.com;3. Department of Computer Applications, GNIMT, Ludhiana, India |
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Abstract: | ABSTRACTCervical 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. |
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Keywords: | Deep neural networks Pap-smear ensemble methods particle swarm optimization cervical cancer detection image classification support vector machine transfer learning |
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