Journal of Guangxi Normal University(Natural Science Edition) ›› 2016, Vol. 34 ›› Issue (2): 61-66.doi: 10.16088/j.issn.1001-6600.2016.02.009

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Research on Cervical Cell Image Feature Extraction and Recognition

LIU Yanhong, LUO Xiaoshu, CHEN Jin, GUO Lei   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004,China
  • Received:2015-12-15 Online:2016-06-25 Published:2018-09-14

Abstract: Cervical smear examination is one of the most effective means of diagnosis of cervical cancer, while the traditional cervical cell recognition system has significant limitations, with low false-negative and false-positive rates. Firstly, morphological characteristics and the gray values of pole in cervical cells are extracted. Then AdaBoost-SVM feature fusion classifier is used to classify the cervical cells in order to improve the efficiency and accuracy of diagnosis of cervical smears. The research results show that the combination of extraction method and multi-feature fusion AdaBoost-SVM classifier can significantly improve the efficiency and accuracy of cervical smear screening, and can reducethe misdiagnosis rate of cervical cancer.

Key words: polar radius, gray median in value, support vector machine, AdaBoost, AdaBoost-SVM classifier

CLC Number: 

  • TP391
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