Abstract
Digital image processing is a prominent tool which is used by radiologists to diagnose the complicated tumor. Magnetic resonance imaging, CT scans, X-rays, etc., are examined and analyzed by extracting the meaningful/accurate information from them. Diagnosing the brain tumor with accuracy is the most critical task. The survival of the infected patients can be increased if the tumor is detected earlier. In this research paper, an ensemble approach is proposed to classify the benign and malignant MRI of the brain. The total of 150 image slices from the Harvard Brain Atlas dataset is utilized in the ratio of 60:40 for training and testing the proposed method. Otsu’s segmentation has been applied to segment the tumor from the skull. Then, the hybrid features including shape, intensity, color and textural features of the MRI are extracted. Decision trees, k-nearest neighbor and support vector machine classifiers are applied separately on the feature set. Then, the stacking model is applied to combine the outcome/prediction of each classifier and gives the final result. The proposed methodology is validated on an open dataset and achieved 97.91% average accuracy, 88.89% precision and 94.44% sensitivity. When compared with other existing methodologies, better accuracy is achieved by this approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Summers, D.: Harvard whole brain atlas: www.med.har-vard.edu/AANLIB/home.html. Last accessed 12-04-2020
Trigui, R., Mitéran, J., Walker, P.M., Sellami, L., Hamida, A.B.: Automatic classification and localization of prostate cancer using multi-parametric MRI/MRS. Biomed. Signal Process Control 31, 189–198 (2017)
Javeria, A., Sharif, M., Yasmin, M., Fernandes, S.L.: A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn. Lett. (2017)
Hemanth, D.J., Anitha, J.: Modified genetic algorithm approaches for classification of abnormal magnetic resonance brain tumour images. Appl. Soft Comput. 75, 21–28 (2019)
Hunnur, S. Raut, Kulkarni. S.: Implementation of image processing for detection of brain tumors. In: International Conference on Intelligent Computing and Control Systems (ICICCS), 2017
Chaplot, S., Patnaik, L., Jagannathan, N.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process Control 1(1), 86–92 (2006)
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., Ahuja, C.K.: A package-SFERCB-segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors. Appl. Soft Comput. 47, 151–167 (2017)
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)
Nabizadeh, N., Kubat, M.: Brain tumors detection and segmentation in MR images: Gabor wavelet versus statistical features. Comput. Electr. Eng., pp. 286–301 (2015)
Kadam, M., Dhole, A.: Brain tumor detection using GLCM with the help of KSVM. Int. J. Eng. Tech. Res. (IJETR) 7(2) (2017)
https://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/#ensemblevoteclassifier. Last accessed 28-04-2020
Huang, M., Yu W., Zhu, D.: An improved image segmentation algorithm based on the Otsu method. In: 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 135–139, 2012
Kaur, R., Doegar, A.: Localization and classification of brain tumor using machine learning & deep learning techniques. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(9S) (2019)
https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm. Last accessed 01-09-2020
https://www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn. Last accessed 01-09-2020
https://www.javatpoint.com/machine-learning-decision-tree-classification-algorithm. Last accessed 10-04-2020
https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/. Last accessed on 26-07-2020
https://towardsdatascience.com/decision-tree-in-machine-learning-e380942a4c96. Last accessed 12-04-2020
https://www.scholarpedia.org/article/Ensemble_learning. Last accessed on 26-08-2020
Lv, Y., et al.: A classifier using online bagging ensemble method for big data stream learning. Tsinghua Sci. Technol. 24(4), 379–388 (2019)
Galar, M., Fernandez, A., Barrenechea, E., Bustince H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) 42(4), 463–484 (2012)
https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/. Last accessed 26-08-2020
Sudharani, K., Sarma, T.C., Prasad, K.S.: Advanced morphological technique for automatic brain tumor detection and evaluation of statistical parameters. Procedia Technol., pp. 1374–1387 (2016)
Nabizadeh, N., John, N., Wright, C.: Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation. Expert Syst. Appl., pp. 7820–7836 (2014)
Subashini, M.M., Sahoo, S.K., Sunil, V., Easwaran, S.: A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Syst. Appl., pp. 186–196 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, R., Doegar, A., Upadhyaya, G.K. (2022). An Ensemble Learning Approach for Brain Tumor Classification Using MRI. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_53
Download citation
DOI: https://doi.org/10.1007/978-981-16-1740-9_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1739-3
Online ISBN: 978-981-16-1740-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)