Abstract
Diagnosis diseases at an untimely phase are a challenging task due to the lack of unfitted segmentation process. This paper focuses on developing an automatic recognition of the brain tumor and pancreatic cancer with precised segmentation and classification process. The proposed k-NN classifier composes of the three stages, namely, (a) Median filtering model for image preprocessing (b) Fuzzy C-segmentation model for accurate segmented image and (c) Gray Level Co-occurrence Matrix (GLCM) for selecting relevant features. The refined features are then given as input to k-NN classifier. The determination of k value clearly emancipates the classes. The proposed classifier tests on the images from the Harvard Medical School database and the Cancer Imaging Archive repositories. Experimental computation is done using metrics like precision, accuracy, specificity, and recall. The results have proved that our proposed classifier outperforms better than prior classifiers SVM, Naïve Bayes, and Probability Neural Network.
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Acknowledgements
The author would like to thank the Kalasalingam University for providing financial help under the University Research Fellowship. We also would like to thank the Department of Electronics and Communication Engineering of Kalasalingam University, Tamil Nadu, India for permitting to use the computational facilities available in the Centre for Research in Signal Processing and VLSI Design which was setup with the support of the DST, New Delhi under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013).
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Reddy, D.J., Arun Prasath, T., Pallikonda Rajasekaran, M., Vishnuvarthanan, G. (2019). Brain and Pancreatic Tumor Classification Based on GLCM—k-NN Approaches. In: Bhaskar, M., Dash, S., Das, S., Panigrahi, B. (eds) International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 846. Springer, Singapore. https://doi.org/10.1007/978-981-13-2182-5_28
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DOI: https://doi.org/10.1007/978-981-13-2182-5_28
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