Machine Learning Approach for Prediction of Cervical Cancer

Abstract

Women in the world are suffering from many diseases among those diseases Cervical Cancer is also mentioned globally. Every year many cancer cases are being registered throughout the world. Cervical cancer is ranked fourth of all the other common cancers according to WHO. Prediction of this cancer in its early stages can be cured, avoiding the death rate. Many people are less aware of this type of cancer as this disease is symptom less. Performance of screening test in regular bases cancerous cells can be detected in its early stages which reduces the mortality rate of people every year. There are many medical approaches for the prediction of this cancer like pap-smear test, colposcopy, biopsy, HPV test or HPV DNA test and other screening tests are performed. These medical methods are combined with the Artificial Intelligence for less false rate and more accurate results. This paper considers pap-smear test images for the prediction of cancerous cells combined with Deep Learning techniques for more efficient results. Convolution Neural Networks (CNN’s) ResNet50 pre-trained model for the prediction of cancerous cells which produces accurate results. The proposed work classifies the cells from the inputted images. This cancer can be cured when it is in the initial stages, the identified abnormal cells helps us for the further treatment. The proposed methodology classifies all the classes with 74.04% of accuracy in prediction of cells for maximum number of epochs. Also in addition, it specifies the class of the testing image.