30Nov 2016

Severity Analysis of Cervical Cancer in Pap Smear Images by using EEETCM, ERSTCM & CFE method based Texture Features and Hybrid Kernel based Support Vector Machine Classifier.

  • Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India.
  • Director, Department of MCA, STET Women\'s College, Mannargudi.
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Classification of medical imagery is a difficult and challenging process due to the intricacy of the images and lack of models of the anatomy that totally captures the probable distortions in each structure. Cervical cancer is one of the major causes of death among women in worldwide. Proper and timely diagnosis can prevent the life to some level. Due to its importance, the aim of the paper is to investigate about the classification of Abnormal Cell of the pap smear image by using individual and combining individual feature extraction method with the classification technique. In this paper three feature extraction methods were used: From that three, two were individual feature extraction methods namely Effective Extending Enriched Texton Co-Occurrence Matrix (EEETCM) & Enriched Rough Set Texton Co-Occurrence Matrix (ERSTCM) and remained one was combining individual feature extraction method named asConcatenated Feature Extraction (CFE). The CFE method represents all the individual feature extraction method of EEETCM & ERSTCM features are combining together as one feature to assess their joint performance. Then these three feature extraction methods are tested over Hybrid Kernel based Support Vector Machine (HKSVM) Classifier. This Examination was conducted over a set of single cervical cell based pap smear images. The dataset contains four classes of images, with a total of 512 images. The distribution of number of images per class is not uniform. Then the performance was evaluated inboth the individual and combining individual feature extraction method with the classification techniques by using the statistical parameters of sensitivity, specificity & accuracy. Hence the resultant value of the statistical parameters described in individual feature extraction method with the classification technique, proposed ERSTCM+HKSVM Classifierhad given the better results than the other EEETCM+HKSVM Classifier and combining individual feature extraction method with the classification technique described, proposed CFE+HKSVM Classifier had given the better results than other EEETCM+HKSVM & ERSTCM+HKSVM classifiers.

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[S. Athinarayanan and M. V. Srinath. (2016); Severity Analysis of Cervical Cancer in Pap Smear Images by using EEETCM, ERSTCM & CFE method based Texture Features and Hybrid Kernel based Support Vector Machine Classifier. Int. J. of Adv. Res. 4 (Nov). 2451-2464] (ISSN 2320-5407). www.journalijar.com

S. Athinarayanan
Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India

DOI:

Article DOI: 10.21474/IJAR01/3099      
DOI URL: https://dx.doi.org/10.21474/IJAR01/3099

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