Clasificación de células cervicales con Máquinas de Soporte Vectorial empleando rasgos del núcleo

Abstract

The conventional cytology is one of the most used techniques, being widely accepted, inexpensive, and with control mechanisms. With the objective of alleviating the workload of to experts, some researchers have proposed the development of tools for computer vision to detect and classify the transformations in the cells of the cervix region. In this paper, is presented the use of the support vector machines (SVM) as a computational method for classification of cervical cells in normal and abnormal conditions, based solely on the extracted features of the region occupied by the nucleus without use of the characteristics of the cytoplasm. The importance of this approach lies in that the nuclei are the region that can be more easily segmented into complex images Pap smear. These images show a high degree of overlapping cells and is difficult to distinguish the exact boundaries of the regions occupied by the cytoplasm. As of study between the linear and RBF kernels on function AUC measures as F, H and half negative predictability was found that performed well RBF values keeping 0.91% AUC. The results indicate a reduction from the rate of false negative Pap test. H mean was used in order to compare the results of SVM with RBF kernel respect to other investigations, obtaining 91.28% above the same.

Keywords

• cervical cells
• classification
• pap smears
• support vector machines