Abstract
40 years ago, uterine cervix cancer represented one of the greatest threats of cancer death among women. With continued advances in medicine and technology, deaths from this disease have declined significantly. The investigations concerning this issue have been determined key symptoms to detect the disease in time to give timely treatment. Conventional cytology is one of the most commonly used techniques being widely accepted because it´s inexpensive, and provide many control mechanisms. In order to alleviate the workload to specialists, some researchers have proposed the development of computer vision tools to detect and classify the changes in the cells of the cervix region. This research aims to provide researchers with an automatic classification tool applicable to the conditions in medical and research centers in the country. This tool classifies the cells of the cervix, based solely on the features extracted from the nucleus region and reduces the rate of false negative Pap test. From the study, a tool using the technique k-nearest neighbors with distance Manhattan, which showed high performance while maintaining AUC values greater than 91% and reaching 97.1% with a sensitivity of 96% and 88% of obtained specificity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maisanava, J.M.L., Soriano, J.Á.M., Murcia, A.P.: Citología Exfoliativa Cervicovaginal (Método de Papanicolaou). Servicio de Anatomía Patológica del Hospital Obispo Polanco de Truel. Boletín Oncológico, vol. 8, pp. 46–54 (1998)
Lorenzo-Ginori, J.V., Rodríguez-Santos, I.: Aplicación de técnicas de visión computacional en la prueba de Papanicolaou. Medicentro Electrónica 16(3), 196–198 (2012)
Campo, P., Bonilla, L.J., Calderon, A.: Cáncer cervical: Citología en base líquida, convencional y otras pruebas de tamizaje. Revista Repertorio de Medicina y Cirugía 21(3), 155–164 (2012)
Ramos, C.M.Á.: Sistema de reconocimento y clasificación de agentes patógenos de nosemosis. In: Departamento de Ingeniería del Software e Inteligencia Artificial, Universidad Complutense de Madrid: Madrid, España (2010)
Lorenzo-Ginori, J.V., Curbelo-Jardines, W., López-Cabrera, J.D., Huergo-Suárez, Sergio B.: Cervical cell classification using features related to morphometry and texture of nuclei. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 222–229. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41827-3_28
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)
Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: An automated cervical pre-cancerous diagnostic system. Artif. Intell. Med. 42(1), 1–11 (2008)
Huang, P.-C., Chan, Y.-K., Chan, P.-C., Chen, Y.-F., Chen, R.-C., Huang, Y.-R.: Quantitative assessment of pap smear cells by pc-based cytopathologic image analysis system and support vector machine. In: Zhang, D. (ed.) ICMB 2008. LNCS, vol. 4901, pp. 192–199. Springer, Heidelberg (2007). doi:10.1007/978-3-540-77413-6_25
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. Comput. Biol. Med. 39(1), 69–78 (2009)
Mas, J.A.G., et al.: Evaluación de dispositivos automatizados para diagnóstico citológico en la prevención del cáncer de cérvix. Revista Española de Patología 35(3), 301–314 (2002)
Patten, S.F., et al.: The AutoPap 300 QC system multicenter clinical trials for use in quality control rescreening of cervical smears. Cancer Cytopathol. 81(6), 337–342 (1997)
Troni, G.M., et al.: Quality control of the autopap screening system employed as a primary screening device: rapid review of smears coded as no further review. Tumori J. Exp. Clin. Oncol. 92(4), 276 (2006)
Soler Font, I., et al.: Aplicación de la lectura automatizada de citología ginecológica. el punto de vista de los citotécnicos. Revista Española de Patología 43(2), 69–72 (2010)
Aida, J.A.O., Moretti, M.M.: Desarrollo de un aplicativo de software, con acceso remoto vía web, orientado a mejorar la calidad del diagnóstico de las pruebas de Papanicolau, utilizando algoritmos computacionales de procesamiento digital de imágenes, in Facultad de Ingeniería, Universidad Peruana de Ciencias Aplicadas, Lima, Perú (2015)
Plissiti, M.E., Nikou, C.: Cervical cell classification based exclusively on nucleus features. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012. LNCS, vol. 7325, pp. 483–490. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31298-4_57
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)
Plissiti, M.E., Nikou, C., Charchanti, A.: Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recogn. Lett. 32(6), 838–853 (2011)
Plissiti, M.E., et al.: Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Trans. Inf Technol. Biomed. 15(2), 233–241 (2011)
Jantzen, J., et al.: Pap-smear benchmark data for pattern classification. In: Proceedings of NiSIS 2005, pp. 1–9. Nature inspired Smart Information Systems (NiSIS), Albufeira (2005)
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 Humanap, p. 67. Universidad Nacional Mayor de San Marcos, Lima (2001)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
García, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Rodríguez-Vázquez, S. (2017). Support to the Diagnosis of the Pap Test, Using Computer Algorithms of Digital Image Processing. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-62434-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62433-4
Online ISBN: 978-3-319-62434-1
eBook Packages: Computer ScienceComputer Science (R0)