Cervical Cancer Prediction Using Deep Neural Network

  • C. Meenu Kumari, R. Bhavani, S. Padmashree, R. Priya

Abstract

The most common and severe disease among women is cervical cancer (CC). Predicting CC is at risk since screening is not possible at an earlier stage. Physicians collect the cervical cells and, by manual intervention, predict cancer types. This approach affects the prediction rate because of human negligence, highly cost-effective, and time-consuming. In this paper, we present an automatic CC prediction using a deep neural network classifier to overcome the issue. The proposed work comprises four stages namely, pre-processing, outlier elimination, dimension reduction, and prediction. Initially, remove the missing data. Second, eliminate the non-matched data based on similarity values. Third, apply the principal component analysis (PCA) to overcome the restrictions caused by high dimensionality data. Finally, input the reduced dimension dataset to the proposed deep neural network classifier (DNN) and classify the input data as normal or abnormal in the classifier. We execute the efficiency of the suggested approach using Python and analyze effectiveness with different metrics.

Published
2021-09-30
How to Cite
C. Meenu Kumari, R. Bhavani, S. Padmashree, R. Priya. (2021). Cervical Cancer Prediction Using Deep Neural Network. Design Engineering, 15667-15679. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/4933
Section
Articles