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Article history: Received: 15 February, 2018 Accepted: 17 July, 2018 Online: 05 August, 2018 Early detection and treatment of cervical cancer is crucial to patients’ recovery with a reported success rate of nearly 100%. Presently, Pap smear test which is a visual inspection of cells collected from the ectocervix is the screening tool mainly used in cancer prevention programs. The Pap smear is relatively easy to handle however, it is time-consuming and requires wet fixation of the cytological material. Thus, there is great demand for an automated-screening system that exhibits high sensitivity, high specificity and highthroughput. Hence, a textural based cervical cancer classification system has been developed in this research work. The wavelet transform was used to denoise 120 Pap smear images to enhance its visual quality while the images were segmented using the morphological operations. Eight textural features of GLCM that serve as inputs into the kNN and SVM classifiers were extracted from each of the images and the performance was evaluated using accuracy, sensitivity and specificity. The result of the developed system shows that clustering shade SVM classifier out-performs entropy k-NN classifier in terms of classification accuracy of 90.0% and 88.3% respectively and vice visa in terms of sensitivity and specificity.

Textural Analysis of Pap Smears Images for k-NN and SVM Based Cervical Cancer Classification System