Abstract
Cervical cancer is the most common cancer among the women. Pap smear screening is the most effective test for detecting the cervical precancerous. But this process requires a long time to complete and also may be an erroneous procedure. In this paper, an automated cervical cancer detection method is presented. This method introduces adaptive median filter to remove impulse noises from the Pap smear images and then uses bi-group enhancer to discriminate the nuclei pixels from other object pixels. Then, segmentation methodology is presented to separate the nucleus regions from the cervical smear images. Two clustering-based classifiers, minimum distance and K-nearest neighbor classifiers, have been used in the classification phase for verifying the performance. The technique was evaluated using 158 Pap smear images from DTU/HERLEV Pap smear benchmark database. The accuracy of the detection method is 92.37 and 98.31 % for minimum distance and K-nearest neighbor classifiers, respectively.
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Acknowledgments
The work presented here is being conducted in the Biomedical Infrared Imaging Laboratory of Tripura University (A Central University). The first author is thankful to the Biometrics Laboratory of Computer Science Engineering Department, Tripura University (A Central University) for providing necessary infrastructural facility to carry out the work.
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Paul, P.R., Bhowmik, M.K., Bhattacharjee, D. (2015). Automated Cervical Cancer Detection Using Pap Smear Images. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_23
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DOI: https://doi.org/10.1007/978-81-322-2217-0_23
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