PERFORMANCE ANALYSIS OF DYSPLASIA DIAGNOSIS FOR CERVICAL CANCER

Author(s):

Banu Priya.C , Christian college of Engineering and Technology, Oddanchathram, India.; Sathis Kumar.R, Christian college of Engineering and Technology, Oddanchathram, India.

Keywords:

Feed Forward Artificial Neural Network, Water shed Segmentation, Local Binary Pattern, Gray level Co-occurrence matrix, Wavelet Transform

Abstract:

Cancer cervix is the second most common cancer in women in the world, while it is the leading cancer in women in the developing countries. Globally, 15% of all cancers′ in females are cervical cancers′, while in Southeast Asia, cancer cervix accounts for 20%-30% of all cancers′. Cancer of cervix is a major cause of death in women living in developing countries. Unlike most other malignancies, cancer of cervix is readily preventable when effective programs are conducted to detect and treat its precursor lesions. Since the introduction of Pap test, a dramatic reduction has been observed in the incidence and mortality of invasive cervical cancer worldwide. Conventional methods used Multimodal Entity Co-reference for combining various tests to perform disease classification and diagnosis. Its performance is degraded due to its low sensitivity and specificity. In this project, we propose watershed segmentation algorithm to segment the abnormal region and this abnormal regions are classified into normal or cancer using feed forward neural network classifier. The performance of the proposed system is analyzed in terms of sensitivity, specificity and accuracy.

Other Details:

Manuscript Id	:	IJSTEV2I10056
Published in	:	Volume : 2, Issue : 10
Publication Date	:	01/05/2016
Page(s)	:	225-231

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