Diagnosing cervical cell images using pre-trained convolutional neural network as feature extractor

Jonghwan Hyeon, Ho-Jin Choi, Byung Doo Lee, Kap No Lee

Abstract: Cervical cancer is a disease that affects 266,000 deaths worldwide and is the fourth highest incidence of cancer in women. This cancer can be diagnosed through a Pap smear, where a cytopathologist observes a microscopic image of the cervix cells to determine whether the patient is normal or abnormal. The sensitivity and specificity of the Pap smear is known to be respectively 53.4% and 69.2%. Since the test is related to the patient's life, it is important to improve the accuracy of the test. A variety of systems have been proposed to help judge experts to improve the accuracy of tests in the medical field, but the development of these systems has been limited to areas where digitized test data are clearly present. In this paper, we design and propose a model that automatically classifies normal/abnormal states of cervical cells from microscopic images using convolutional neural network and several machine learning classifiers. As a result, the support vector machine showed the best performance with a 78% F1 score.