Rodríguez VS, Martínez BAV

Language: Spanish
References: 17
Page: 224-238
PDF size: 286.71 Kb.

ABSTRACT

Cervix cancer is one of the biggest threats of cancer death among women. With continued advances in medicine and technology, deaths from the disease have fallen significantly. The investigations concerning this issue have determined key symptoms to detect the disease in time to give timely treatment. Conventional cytology is one of the most widely used techniques, being widely accepted, inexpensive, and with control mechanisms. In order to alleviate the workload of specialists, some researchers have proposed the development of computer vision tools to detect and classify the changes in the cells of the cervical region. This research aims to provide a tool for automatic classification, applicable to medical conditions and research centers of the country. This tool should be able to classify the cells of the cervix, based solely on the features extracted from the core region without using the characteristics of the cytoplasm, so that the rate of false negative Pap test is reduced. From the study, a tool is obtained using the k nearest-neighbors manhattan distance technique, which showed a high performance maintaining AUC values greater than 91% and reaching 97.1% over classifiers SVM and RBF Network, which were also analyzed.

REFERENCES

1. Lorenzo J.V Rodríguez I. Aplicación de técnicas de visión computacional en la prueba de Papanicolaou. Medicentro Electrónica. 2012. 16(3): p. 196-198.

2. Acosta L.F. El diagnóstico temprano es garantía de vida. In Periódico Granma. 2012: Ciudad de la Habana.

3. Plissiti M, Nikou C. Cervical Cell Classification Based Exclusively on Nucleus Features. In Image Analysis and Recognition. Campilho A, Kamel M, Editors. 2012, Springer Berlin Heidelberg. p. 483-490.

4. Plissiti M.E, Nikou C. On the importance of nucleus features in the classification of cervical cells in Pap smear images. University of Ioannina. 2012.

5. Riana D, Murni A. Performance evaluation of Pap smear cell image classification using quantitative and qualitative features based on multiple classifiers. In International Conference on Advanced Computer Science and Information Systems, ACSIS. 2009.

6. Mat-Isa N.A, Mashor M.Y, Othman N.H. An automated cervical pre-cancerous diagnostic system. Artificial Intelligence in Medicine. 2008. 42(1): p. 1-11.

7. Huang P.-C, et al. Quantitative Assessment of Pap Smear Cells by PC-Based Cytopathologic Image Analysis System and Support Vector Machine, In Medical Biometrics. D. Zhang, Editor. 2007, Springer Berlin Heidelberg. p. 192-199.

8. Marinakis Y, Dounias G, Jantzen J. Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine. 2009. 39(1): p. 69-78.

9. Plissiti M.E, Nikou C, Charchanti A. Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognition Letters. 2011. 32(6): p. 838-853.

10. Plissiti M.E, et al. Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering. IEEE Transactions on information technology in biomedicine. 2011. 15(2): p. 233-241.

11. Lorenzo J.V, et al. Cervical Cell Classification Using Features Related to Morphometry and Texture of Nuclei, in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 2013, Springer. p. 222-229.

12. Velezmoro G.A.B, Villafuerte D.F. Factores de riesgo que pronóstican el hallazgo de citologías cervicales anormales en dos poblaciones: mujeres de obreros de construcción civil vs. mujeres control en la posta médica "Construcción Civil" ESSALUD, de junio a septiembre del 2000, in Facultad de Medicina Humana. 2001, Universidad Nacional Mayor de San Marcos: Lima, Perú. p. 67.

13. Jantzen J, et al. Pap-smear Benchmark Data For Pattern Classification. In Proc. NiSIS 2005. 2005, Nature inspired Smart Information Systems (NiSIS): Albufeira, Portugal. p. 1-9.

14. Bamber D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology. 1975. 12(4): p. 387-415.

15. Hanley J.A, McNei lB.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982. 143(1): p. 29-36.

16. Demšar J. Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research. 2006. 7: p. 1-30.

17. García S, Herrera F. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons. Journal of Machine Learning Research. 2008. 9: p. 2677-2694.