Automating Papanicolaou Test Using Deep Convolutional Activation Feature

Abstract

Cervical cancer is the women's fourth most common cancer worldwide, with 266,000 deaths in a year. Cervical cancer can be diagnosed by the Papanicolaou test. In this test, a cytopathologist observes a microscopic image of the cervix cells and decides whether the patient is abnormal or not. According to research, the accuracy of the cervical cytology is reported as 89.7%. Because it is associated with the patient's life, it is important to improve the accuracy of this test. Many systems have been proposed to help judge experts to improve the accuracy of tests in the medical field, but development has been limited to areas where there are cleanly quantified test data. In this paper, we design and train a model to automatically classify the normal/abnormal state of cervical cells from microscopic images by using a convolutional neural network and several machine learning classifiers. As a result, the support vector machine achieves the highest performance with 78% F1 score.