Cervical Cancer Risk Classification Based on Deep Convolutional Neural Network
Durrabida Zahras, Zuherman Rustam · 2018
To meet the challenge of the increasing types of disease in this modern era, technology plays a very important role in health research. Women's health has become a major concern because of the increasing rates of cervical cancer because it can be a deadly disease. In this study, we will use deep convolutional neural networks to find the accuracy in classifying cervical cancer data on four different types of methods. The cervical cancer data are represented by 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. The result with deep learning method is quite encouraging, we can see that each data were correctly classified with the total accuracy reach almost 90% for each target.