Abstract
Medical imaging plays an important role to generate images of mankind for clinical and medical research. In this paper, we are focused on the detection of brain tumor using MRI images, one of the modalities of medical imaging. Basically, tumor is an abandoned growth of tissues in any portion of the human body. Nowadays, automatic detection of brain tumor is the foremost area for research. In this paper, we proposed a system that checks whether the tumor is present or not; if the tumor is present, then classify the tumor. For detection and classification of brain tumor, we have done an experiment on 150 T1-weighted MRI brain images. The supervised classification has applied for classification so that training set is created using texture feature that is extracted with GLCM and DWT methods; for feature selection, principle component analysis has been used. The experimental results show KSVM gives 97% accuracy to classify the brain tumor. We have also calculated the area and volume of the tumor to find stages of the tumor.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zulpe, N., Pawar, V.: GLCM textural features for brain tumor classification. IJCSI Int. J. Comput. Sci. Issues 9(3), 354–359 (2012)
Jafarpour, S., Sedghi, Z., Amirani, M.C.: A robust brain MRI classification with GLCM features. Int. J. Comput. Appl. 37(12), 1–5 (2012)
Sharma, M., Mukherjee, S.: Fuzzy c-means, anfis and genetic algorithm for segmenting astrocytoma-a type of brain tumor. IAES Int. J. Artif. Intell. 3(1), 16 (2014)
Rathi, V.P., Palani, S.: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis (2012). arXiv preprint arXiv:1208.2128
Sharma, M., Singh, S.: A modified and improved method for detection of tumor in brain cancer. Int. J. Comput. Appl. 91(6) (2014)
Biradar, C., Shantkumari: Measurement based human brain tumor recognition by adapting support vector machine. IOSR J. Eng. (IOSRJEN) 3(9), 26–31 (2013). e-ISSN: 2250-3021, p-ISSN: 2278-8719
Chandra, S., Bhat, R., Singh, H., Chauhan, D.S.: Detection of brain tumors from MRI using gaussian RBF kernel based support vector machine. IJACT 1(1), 46–51
Singh, D., Kaur, K.: Classification of abnormalities in brain MRI images using GLCM, PCA and SVM. IJEAT (1), 243–248 (2012)
Kharrat, A., Halima, M.B., Ayed, M.B.: MRI brain tumor classification using support vector machines and meta-heuristic method. In: 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 446–451. IEEE (2015)
Albregtsen, F.: Statistical texture measures computed from gray level cooccurrence matrices. Image Processing Laboratory, Department of Informatics, University of Oslo, pp. 5 (2008)
Acharya, U.R., Ng, E.Y.K., Tan, J.H., Sree, S.V.: Thermography based breast cancer detection using texture features and support vector machine. J. Med. Syst. 36(3), 1503–1510 (2012)
Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3(5), 1 (2013)
Zhou, M., Hall, L.O., Goldgof, D.B., Gatenby, R., Gillies, R.J.: A texture feature ranking model for predicting survival time of brain tumor patients. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4533–4538. IEEE (2013)
Sawakare, S., Chaudhari, D.: Classification of brain tumor using discrete wavelet transform, principal component analysis and probabilistic neural network. Int. J. Res. Emerg. Sci. Technol. 1 (2014)
Sandhya, G., Giri, K., Savitri, S.: A novel approach for the detection of tumor in MR images of the brain and its classification via independent component analysis and Kernel support vector machine. Imaging Med. (2017)
Gondal, A.H., Khan, M.N.A.: A review of fully automated techniques for brain tumor detection from MR images. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 5(2), 55 (2013)
Sharma, M., Mukharjee, S.: Brain tumor segmentation using hybrid genetic algorithm and artificial neural network fuzzy inference system (anfis). Int. J. Fuzzy Logic Syst. 2(4), 31–42 (2012)
Demirhan, A., Toru, M., Guler, I.: Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks (2014)
Meenakshi, R., Anandhakumar, P.: An improved local ternary pattern based tumour classification of MRI of brain. Res. J. Appl. Sci. (9), 99–103 (2014)
Dataset Sources
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pareek, M., Jha, C.K., Mukherjee, S. (2020). Brain Tumor Classification from MRI Images and Calculation of Tumor Area. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-0751-9_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0750-2
Online ISBN: 978-981-15-0751-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)