Performance Evaluation of Pap Smear Cell Image Classification Using Quantitative and Qualitative Features Based on Multiple Classifiers

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Abstract— This paper presents the results of a study on Pap smear cell image classification using qualitative features, as an effort to improve the results of the previous study using quantitative features especially in the performance of the case using all(7)-class category of diseases.  Three good classifiers have been chosen and they include the Naïve Bayes (NB), Multi-Layer Perceptrons (MLPs), and Decision Tree learning algorithm (J48). Twenty quantitative features and seven categories of Pap smear cell image class have been used in this study. Three classes of which are normal cell image class categories that include: Normal Superficial, Normal Intermediate, and Normal Columnar, and the other four classes are categories of abnormal cell image class that include: Mild (Light) Dyplasia, Moderate Dysplasia, Severe Dysplasia and Carcinoma In Situ.  The conversion of quantitative to qualitative features and the image classification process were done with the aid of the Weka software.  The experimental study shows that in all(7)-class classification based on the qualitative features and using the three classifiers with the Weka Correctly Classification Instances (CCI) and Kappa Coefficient classification performance measures, the Naïve Bayes classifier performs the best with the CCI of 73.33% and the Kappa Coefficient of 0.69.  If it is compared to the results of using the quantitative features, the Multiple Layer Perception classifier has performed the best with the CCI of 71.43% and the Kappa Coefficient of 0.67, while the Naïve Bayes produced the CCI of 66.67% and the Kappa Coefficient of 0.61.  The use of qualitative features can improve the performance of the CCI approximately 2% to 7% and of the Kappa coefficient approximately 0.02 to 0.08. 

Bidang ilmu
Image Processing


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