DETECTION AND CLASSIFICATION OF BRAIN TUMOR USING ML

Avirup Chowdhury, Indrajit Das, Avipsa Roy Chowdhury, Arnab Halder

Abstract


Brain tumor detection and classification is the most difficult and tedious task in the area of medicinal image preparing. MRI (Magnetic Resonance Imaging) is a medicinal procedure, generally adopted by the radiologist for representation of inner structure of the human body with no surgery. MRI gives abundant data about the human delicate tissue, which helps in the conclusion of brain tumor. Precise segmentation of MRI image is basic for the conclusion of brain tumor by computer supported clinical device. After segmentation of brain MRI images, tumor is grouped to be either malignant or benign, which is a confounded task since complexity varies in proportion to the tumor tissue traits like its shape, size, gray level intensities and location. Taking into account the aforesaid challenges, this paper is focused towards the design of an optimal and more accurate way for the detection of tumor from brain MRI scans and if it confirms the presence of tumor then it is focused on evaluating its stage, i.e., benign or malignant. We have experimentally shown that our proposed methodology has a greater accuracy than other existent methods for classifying tumor type to be either as Malignant or Benign since the maximum accuracy for detection of malignant tumor is 99.02% and for Benign tumor is 99.67%.

Keywords


malignant tumor; benign tumor; anisotropic diffusion filter; support vector machine; morphological operation

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References


J. Selvakumar, A. Lakshmi, T. Arivoli, “ Brain Tumor segmentation and its area calculation in brain MR images using K – mean clustering and Fuzzy C - mean algorithm”, International Conference on Advances in Engineering, Science and Management (ICAESM), 2012, pp. 186 – 190, ISBN: 978-81-909042-2-3.

A. Islam, S.M.S Reza, K.M Iftekharuddin, “ Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors”, IEEE Transactions on Biomedical Engineering, Vol. 60, Issue 11, 2013, pp. 3204 -3215, ISSN: 1558-2531.

S. Bauer, C May, D Dionysiou, G. Stamatakos, P. Buchler, M. Reyes, “ Multiscale Modeling for Image Analysis of Brain Tumor Studies”, IEEE Transactions on Biomedical Engineering, Vol. 59, Issue 1, 2012, pp. 25 – 29, ISSN: 1558-2531.

R. Ahmmed, A.S. Swakshar, Md. F.Hossain, Md.A. Rafiq, “Classification of Tumors and It Stages in Brain MRI Using Support Vector Machine and Artificial Neural Network”, International Conference on Electrical, Computer and Communication Engineering (ECCE), 2017, pp. 229 - 234, ISBN: 978-1-5090-5627-9.

P.S. Mukambika, K Uma Rani, “Segmentation and Classification of MRI Brain Tumor”, International Research Journal of Engineering and Technology (IRJET), Vol.4, Issue 7, 2017, pp. 683 – 688, ISSN: 2395-0056.

G. Singh, M.A. Ansari, “Efficient Detection of Brain Tumor from MRIs Using K-Means Segmentation and Normalized Histogram”, 1st India International Conference on Information Processing (IICIP), 2016, pp. 1-6, ISBN: 978-1-4673-6984-8.

K. Machhale, H.B.Nandpuru,V. Kapur, L. Kosta, “MRI Brain Cancer Classification Using Hybrid Classifier (SVM-KNN)”, International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 60 -65, ISBN: 978-1-4799-7165-7.

Parveen, A.Singh, “Detection of Brain Tumor in MRI Images, using Combination of Fuzzy c-means and SVM”, 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015, pp. 98 -102, ISBN: 978-1-4799-5991-4.

K. Sudharani, T.C. Sarma, K.S. Rasad, “Intelligent Brain Tumor Lesion Classification and Identification from MRI Images Using k-NN Technique”, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2015, pp. 777- 780, ISBN: 978-1-4673-9825-1.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 12, Issue: 7, Jul 1990, pp. 629-639, ISSN: 0162-8828.

T.S. D Murthy and G. Sadashivappa, “Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor”, International Conference on Advances in Electronics Computers and Communications, 2014, pp. 1-6, ISBN: 978-1-4799-5496-4.

A. Demirhan and ˙I. Guler, “Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation,” Engineering Applications of Artificial Intelligence, volume: 24, Issue: 2, 2011 pp. 358–367, ISSN: 0952-1976.

R. Vijayarajan and S. Muttan, “Discrete wavelet transform based principal component averaging fusion for medical images”, AEU-International Journal of Electronics and Communications(AEU), volume: 69, 2015 pp. 896-902, ISSN: 1434-8411.

P. John, “Brain Tumor Classification Using Wavelet and Texture Based Neural Network”, International Journal of Scientific & Engineering Research, volume:3, Issue:10,2012 pp.1-7, ISSN: 2229-5518.

N Zhang, “Feature Selection based Segmentation of Multi-Source Images: Application to Brain Tumor Segmentation in Multi-Sequence MRI”, Ph.D. Thesis, L’Institut National des Sciences Appliquées de Lyon 2011.

Z.Q. Bian and X.G. Zhang. Pattern Recognition [M]. Beijing: Tsinghua University Press, 2000.

B. Schölkopf and A. J. Smola, Learning with Kernels Support Vector Machines: Regularization, Optimization and Beyond. Cambridge, MA: MIT, 2002.

J. Zhou1, K. L. Chan1, V. F. H. Chong, S. M. Krishnan , “Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine”, IEEE, Engineering in Medicine and Biology 27th Annual Conference,2005, pp. . 6411-6414, ISBN: 0-7803-8741-4.




DOI: https://doi.org/10.26483/ijarcs.v9i2.5807

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