To explore the potential of conventional image processing techniques in the classification of cervical cancer cells, in this work, a co-occurrence histogram method was employed for image feature extraction and an ensemble classifier was developed by combining the base classifiers, namely, the artificial neural network (ANN), random forest (RF), and support vector machine (SVM), for image classification. The segmented pap-smear cell image dataset was constructed by the k-means clustering technique and used to evaluate the performance of the ensemble classifier which was formed by the combination of above considered base classifiers. The result was also compared with that achieved by the individual base classifiers as well as that trained with color, texture, and shape features. The maximum average classification accuracy of 93.44％ was obtained when the ensemble classifier was applied and trained with co-occurrence histogram features, which indicates that the ensemble classifier trained with co-occurrence histogram features is more suitable and advantageous for the classification of cervical cancer cells.

Rajesh Yakkundimath;Varsha Jadhav;Basavaraj Anami;Naveen Malvade

Department of Computer Science and Engineering, Karnataka Lingayat Education Institute of Technology, Hubballi 580027;Visvesvaraya Technological University, Belagavi 590018;Department of Computer Science and Engineering, Karnataka Lingayat Education Institute of Technology,Hubballi 580027;Department of Information Science and Engineering, Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dharwad 580008;Department of Information Science and Engineering, Smt. Kamala & Sri. Venkappa M. Agadi College of Engineering and Technology, Lakshmeshwar 582116

电子科技学刊

[1]Rajesh Yakkundimath;Varsha Jadhav;Basavaraj Anami;Naveen Malvade-.Co-Occurrence Histogram Based Ensemble of Classifiers for Classification of Cervical Cancer Cells)[J].电子科技学刊,2022(03):270-281

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