Research on Feature Selection and Classification Recognition Algorithm of Cervical Cell Image
Abstract
In order to improve the recognition speed of cervical cell and obtain the highest recognition accuracy with the least number of features，this paper innovatively uses the Classification and Regression Trees（CART） algorithm to select features，and then the Particle Swarm Optimization（PSO） algorithm is used to optimize the Support Vector Machine(SVM). Therefore，the PSO-SVM classification algorithm is formed to classify the cells. This paper uses the Herlev dataset to verify the validity of the proposed algorithm. Through the CART feature selection method，9 representative features are successfully extracted from 20 features，and the accuracy of two classifications and seven classifications are above 99%. Further，this paper introduces several other classification and recognition algorithms of cervical cancer cells for simulation comparison. It can be founds that the recognition accuracy of this algorithm is obviously superior when the number of features is small，which indicates that the proposed algorithm is effective. The method effectively reduces the difficulty of artificial feature selection，and ensures that the recognition accuracy of the cells is almost the same as before when the recognition time is reduced. Thus，the proposed algorithm provides an effective method for the diagnosis of cervical cancer diseases.
Keywords: feature extraction, feature selection, CART, PSO-SVM, cervical cell detection
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FERLAYJ， SOERJOMATARAMI， DIKSHITR， et al. Cancer in cadence and mortality worldwide： sources， methods and major patterns [J] .International Journal of Cancer， 2015，136（5）：359— 386.
HONGJG， CHENGDX， YUGP. Characteristics of cell image [J] . Information and Control，1983， 12（2）：28—33， 45.（In Chinese）
DUANJ， HUQH， ZHANGLJ， et al. Multi-label classification feature selection algorithm based on neighborhood rough set[J] . Computer Researchand Development， 2015， 52（1）：56—65.（In Chinese）
NUNOBIKIO， SATOM， TANIGUCHIE， et al. Color image analysis of cervical neoplasia using RGB computer color specification [J] . Analytical and Quantitative Cytology and Histology， 2002，24 （5）：289—294.
VIJAYASHREE R，RAO K. A semi-automated morphometric assessment of nuclei in pap smears using image[J] . Journal of Evoltion of Medical and Dental Sciences，2015，4（53）：63—70.
ZHANG Y. An effective segmentation method for color space of white blood cell image [J] . Journal of Xian Jiaotong University， 1998，32（8）：52—56.（In Chinese）
HUA L，YE Y K. Knowledge-based early cell diagnosis system for lung cancer [J] . Journal of Computer Applications，2000，17（2）： 90—92.（In Chinese）
LU X Q，LI N，CHEN S F. Application of morphology，color features and neural network in recognition of lung cancer cells [J] . Journal of Computer -Aided Design & Computer Graphics，2001，13（1）： 87—92.（In Chinese）
JANTZEN J，NORUP J，DOUNIAS G，et al. Pap-smear benchmark data for pattern classification [J] . Nature Inspired Smart Information Systems（NiSIS 2005），2005：1—9.
HALLINAN J，JACKWAY P. Detection of malignancy associated changes in thionin stained cervical cells [J] . Digital Image Computing and Applications，1995，27（5）：426—431.
PLISSITI M E，NIKOU C. A review of automated techniques for cervical cell image analysis and classification [M] . Netherlands： Biomedical Imaging and Computational Modeling in Biomechanics，2013：1—18.
CHEN Y F，HUANG P C，LIN K C，et al. Semi-automatic segmentation and classification of pap smear cells [J] . Biomedical and Health Informatics，2014，18（1）：94—108.
HARALICK R M，SHANMUGAM K，DINSTEIN I H. Textural features for image classification [J] . Systems，Man and Cybernetics， 1973，12（6）：610—621.
WALKER R F，JACKWAY P，LOVELL B，et al. Classification of cervical cell nuclei using morphological segmentation and textural feature extraction [C] //Conference on Intelligent Information Systems. New Zealand：IEEE，1994：297—301.
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