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Analytical Cellular Pathology
Volume 2016 (2016), Article ID 9535027, 11 pages
Research Article

Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

1College of Computer School, Harbin University of Science and Technology, Harbin 150080, China
2Department of Pathology and Lab Medicines, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA

Received 21 February 2016; Revised 21 March 2016; Accepted 7 April 2016

Academic Editor: Ilary Ruscito

Copyright © 2016 Jie Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500), lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression) classifier was used individually; while the recognition rate was significantly higher (95.642%) when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44%) of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review.