Abstract
Recently computer aided diagnosis is largely used in many clinical processes to detect, predict and analyze many abnormalities. It is clear that in medical image processing, brain tumor classification and detection plays a significant task. MRI gives anatomical structure’s information, and the potential abnormal tissues’ information. Hence in this paper a new system is proposed for detection and classification of brain tumors. The proposed system consists of feature extraction and tumor classification. In feature extraction, Rough set theory (RST) is used and for classification task particle swam optimization neural network (PSONN) is trained and tested in order to classify the MRI brain images into normal and abnormal.
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Vijay, J., Subhashini, J.: An efficient brain tumor detection methodology using K- means clustering algorithm. In: Proceedings of the International conference on Communication and Signal Processing. April 3–5, pp. 653–658 (2013)
Khayati, R., Vafadust, M., Towhidkhah, F., Nabavi, M.: Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using an adaptive mixtures method and Markov random field model’. Comput. Biol. Med. 38, 379–390 (2008)
Gopal, P.N., Sukanesh, R.: Wavelet statistical feature based segmentation and classification of brain computed tomography images IET Image Process. 7, 25–32 (2013)
Maksoud, E.A., Elmogy, M., Al-Awadi, R.: Brain Tumor Segmentation based on a Hybrid Clustering Technique. Egypt. Inf. J. 16(1), 71–81 (2015)
Bhanumurthy, M.Y., Anne, K.: Multiple sclerosis segmentation in brain MR images using modified histon based fast fuzzy C-means. Int. J. Appl. Eng. Res. 10(16), 37826–37833 (2015)
Dahab, D.A., Ghoniemy, S.S.A., Selim, G.M.: Automated brain tumor detection and identification using image processing and probablistic neural network techniques. Int. J. Image Process. Visual Commun. 1, 1–8 (2012)
Gupta, J.M.P., Shringirishi, M.M.: Implementation of brain tumor segmentation in brain mr images using k-means clustering and fuzzy c means algorithm. Int. J. Comput. Technol. 5(1), 54–59 (2013)
Menze, B. H.,Van Leemput, K., Lashkari, D. Weber M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Proceedings of the Medical Image Computing and Computer- Assisted Intervention-MICCAI. Springer, Berlin, pp. 151–159 (2010)
Bhanumurthy, M. Y.,Anne, K.: An automated detection and segmentation of tumor in brain MRI using artificial intelligence. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research. pp. 391–396 (2014)
Mallick, P. K., Satapathy, B. S., Mohanty, M. N., Kumar, S. S.: Intelligent technique for CT brain image segmentation. In: Proceedings of the 2nd International conference on electronics and communication systems (ICECS). IEEE (2015)
Prasad, B. G.,Krishna A. N.: Classification of medical images using data mining techniques. In: Proceedings of the Social Informatics and Telecommunications Engineering. pp-54-59 (2012)
Sapra, P., Singh, R., Khurana, S.: Brain tumor detection using neural network. Int. J. Sci. Modern Eng. 1, 83–88 (2013)
Badran, E. F., Mahmoud, E. G., Hamdy, N.: An algorithm for detecting brain tumor in MRI images. In: Proceedings of the IEEE International Conference on Computer Engineering And Systems. Cairo, Egypt, November-December. pp. 368–373 (2010)
Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)
Kabade, R.S., Gaikwad, M.S.: Segmentation of brain tumour and its area calculation in brain MR images using K-mean clustering and fuzzy C-mean algorithm. Int. J. Comput. Sci. Eng. Technol. 4(5), 524–531 (2013)
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Rajesh, T., Malar, R.S.M. & Geetha, M.R. Brain tumor detection using optimisation classification based on rough set theory. Cluster Comput 22 (Suppl 6), 13853–13859 (2019). https://doi.org/10.1007/s10586-018-2111-5
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DOI: https://doi.org/10.1007/s10586-018-2111-5