Abstract
This paper presents a novel approach of classifying the type of glioma using convolutional neural network (CNN) on 2D MR images. Glioma, most common type of malignant brain tumor, and can be classified according to the type of glial cells affected. The types of gliomas are, namely, actrocytoma, oligodendroglioma and glioblastoma multiforme (GBM). Various image processing and pattern recognition techniques may be used for cancer identification and classification. Though in recent years deep learning has been proved to be efficient in computer aided diagnosis of diseases. Convolutional Neural Networks, a type of deep neural network which is generally used for classification of images, contains multiple sets of conv-pool layers for feature extraction, followed by fully-connected (FC) layers that make use of extracted features for classification.
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
Ideguchi, M., Kajiwara, K., Goto, H., Sugimoto, K., Nomura, S., Ikeda, E., Suzuki, M.: MRI findings and pathological features in early-stage glioblastoma. J. NeuroOncol. 123, 289–297 (2015)
El-Gamal, F., Elmogy, M., Atwan, A.: Current trends in medical image registration and fusion. Egypt. Inform. J. 17, 99–124 (2016). https://doi.org/10.1016/j.eij.2015.09.002
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Zeiler, M., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833 (2014)
Rajnikanth, V., Fernandes, S., Bhushan, B., Sunder, N.: Segmentation and analysis of brain tumor using tsallis entropy and regularised level set. In: 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications. Springer, Singapore (2018)
Hoseini, F., Shahbahrami, A., Bayat, P.: An efficient implementation of deep convolutional neural networks for MRI segmentation. J. Digit. Imaging 31, 738 (2018)
McGuinness, K., O’Connor, N.: A comparative evaluation of interactive segmentation algorithms. Pattern Recognit. 43, 434–444 (2010)
Wang, G., Li, W., Zuluaga, M., Pratt, R., Patel, P., Aertsen, M., Doel, T., David, A., Deprest, J., Ourselin, S., Vercauteren, T.: Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Trans. Med. Imaging. 37, 1562 (2018)
Sadeghi-Naini, A., Suraweera, H., Tran, W., Hadizad, F., Bruni, G., Rastegar, R., Curpen, B., Czarnota, G.: Breast-lesion characterization using textural features of quantitative ultrasound parametric maps. Sci. Rep. 7, 13638 (2017)
Liu, M., Zhang, J., Nie, D., Yap, P., Shen, D.: Anatomical landmark based deep feature representation for MR images in brain disease diagnosis. IEEE J. Biomed. Health Inform. 22, 1476 (2018)
Devkota, B., Alsadoon, A., Prasad, P., Singh, A., Elchouemi, A.: Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Comput. Sci. 125, 115–123 (2018)
Blumenthal, D., Artzi, M., Liberman, G., Bokstein, F., Aizenstein, O., Ben Bashat, D.: Classification of high-grade glioma into tumor and nontumor components using support vector machine. Am. J. Neuroradiol. 38, 908–914 (2017)
Zacharaki, E., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E., Davatzikos, C.: Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. 62, 1609–1618 (2009)
Liu, F., Jang, H., Kijowski, R., Bradshaw, T., McMillan, A.: Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 286, 676–684 (2017)
Roffman, D., Hart, G., Girardi, M., Ko, C., Deng, J.: Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci. Rep. 8, 1701 (2018)
Makde, V., Bhavsar, J., Jain, S., Sharma, P.: Deep neural network based classification of tumourous and non-tumorous medical images. In: International Conference on Information and Communication Technology for Intelligent Systems, pp. 199–206 (2017)
Scarpace, L., Flanders, A.E., Jain, R., Mikkelsen, T., Andrews, D.W.: Data From REMBRANDT. The Cancer Imaging Archive (2017)
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013)
Choromanska, A., Henaff, M., Mathieu, M., Arous, G., LeCun, Y.: The loss surfaces of multilayer networks. In: Artificial Intelligence and Statistic, pp. 192–204 (2015)
Kamnitsas, K., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). https://doi.org/10.1016/j.media.2016.10.004
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Saxena, N., Sharma, R., Joshi, K., Rana, H.S. (2019). Identification of Glioma from MR Images Using Convolutional Neural Network. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-02686-8_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02685-1
Online ISBN: 978-3-030-02686-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)