Abstract
In today’s biomedical scenario, medical automation is improvising and is in demand on an extensive scale. Detecting brain tumor also needs automation for further treatment. There are many brain imaging methods like X-Ray, CT scans, MRI, EEG, and PET. The main aim of these medical imaging techniques is to extract all meaningful and precise information from images with the least error possible. MRI is a non-invasive technique that is currently used for brain analysis, where an experienced doctor has to manually and precisely identify brain tumors in the MRI scans. This paper presents computer-aided deep learning approaches to detect and segment the brain tumor from the MRI images taken from the standard dataset. The proposed work focuses on three main steps like preprocessing of the images, classifying data into the tumor and non-tumorous images, and segmenting the brain tumor region. Convolutional Neural Network (CNN) is a deep learning technique that is mainly used in image classification. In this work, a VGG16 CNN architecture is utilized to classify brain images. Further, computer vision is used to semantically segment the tumorous areas in the MRI scans. The weights of the trained CNN VGG-16 model are used to build bounding boxes and to segment the tumor regions of the MRI scans using the Mask R-CNN model. Mask R-CNN model is a widely used computer vision algorithm that segments and highlights the tumor region in the MRI scan. Thus, using CNN and computer vision techniques, the proposed approach can able to achieve a highly precise medical automation system that can detect and identify brain tumors with improved precision.
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
VGG 16 - convolutional neural network model. https://neurohive.io/en/popular-networks/vgg16/. Accessed: 2021-1-8
Amin, J., Sharif, M., Gul, N., Yasmin, M., & Shad, S. A. (2020). Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognition Letters, 129, 115–122. https://doi.org/10.1016/j.patrec.2019.11.016.
Badža, M.M., Barjaktarović, M.C.: Classification of brain tumors from mri images using a convolutional neural network. Applied Sciences (Switzerland) 10(6) (2020). https://doi.org/10.3390/app10061999.
Charron, O., Lallement, A., Jarnet, D., Noblet, V., Clavier, J. B., & Meyer, P. (2018). Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Computers in Biology and Medicine, 95,. https://doi.org/10.1016/j.compbiomed.2018.02.004.
Cheng, J.: brain tumor dataset (2015). https://doi.org/10.6084/m9.figshare.1512427.v1. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427.
Daimary, D., Bora, M. B., Amitab, K., & Kandar, D. (2020). Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks. Procedia Computer Science, 167, 2419–2428. https://doi.org/10.1016/j.procs.2020.03.295.
Das, S., Aranya, O.F.R., Labiba, N.N.: Brain Tumor Classification Using Convolutional Neural Network. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019 (2019). https://doi.org/10.1109/ICASERT.2019.8934603
Kaldera, H.N., Gunasekara, S.R., DIssanayake, M.B.: Brain tumor Classification and Segmentation using Faster R-CNN. In: 2019 Advances in Science and Engineering Technology International Conferences, ASET 2019 (2019). https://doi.org/10.1109/ICASET.2019.8714263
Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., et al. (2019). Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magnetic Resonance Imaging, 64,. https://doi.org/10.1016/j.mri.2019.05.020.
Khan, H. A., Jue, W., Mushtaq, M., & Mushtaq, M. U. (2020). Brain tumor classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering, 17(5), 6203–6216. https://doi.org/10.3934/MBE.2020328.
Kumar, A., Manikandan, R., & Rahim, R. (2020). A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques. Journal of Computational and Theoretical Nanoscience, 17(4), 1925–1930. https://doi.org/10.1166/jctn.2020.8468.
Kumar, S., Negi, A., Singh, J.N., Gaurav, A.: Brain Tumor Segmentation and Classification Using MRI Images via Fully Convolution Neural Networks. In: Proceedings - IEEE 2018 International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018, pp. 1178–1181 (2018). https://doi.org/10.1109/ICACCCN.2018.8748614
Moeskops, P., Viergever, M. A., Mendrik, A. M., De Vries, L. S., Benders, M. J., & Isgum, I. (2016). Automatic Segmentation of MR Brain Images with a Convolutional Neural Network. IEEE Transactions on Medical Imaging, 35(5), 1252–1261. https://doi.org/10.1109/TMI.2016.2548501.
Parihar, A. S. (2018). A study on brain tumor segmentation using convolution neural network. Proceedings of the International Conference on Inventive Computing and Informatics, ICICI, 2017, 198–201. https://doi.org/10.1109/ICICI.2017.8365336.
Pei, L., Vidyaratne, L., Hsu, W.W., Rahman, M.M., Iftekharuddin, K.M.: Brain tumor classification using 3D convolutional neural network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11993 LNCS, pp. 335–342 (2020). https://doi.org/10.1007/978-3-030-46643-5_33
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251. https://doi.org/10.1109/TMI.2016.2538465.
Rajini, N. H. (2019). Brain tumor image classification and grading using convolutional neural network and particle swarm optimization algorithm. International Journal of Engineering and Advanced Technology, 8(3), 42–48.
Sankari, A., Vigneshwari, S.: Automatic tumor segmentation using convolutional neural networks. In: ICONSTEM 2017 - Proceedings: 3rd IEEE International Conference on Science Technology, Engineering and Management, vol. 2018-Janua, pp. 268–272 (2017). https://doi.org/10.1109/ICONSTEM.2017.8261291
Siar, M., Teshnehlab, M.: Brain tumor detection using deep neural network and machine learning algorithm. In: 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019, pp. 363–368 (2019). https://doi.org/10.1109/ICCKE48569.2019.8964846
Ucuzal, H., Yasar, S., Colak, C.: Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface. In: 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings (2019). https://doi.org/10.1109/ISMSIT.2019.8932761
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11384 LNCS, pp. 61–72 (2019). https://doi.org/10.1007/978-3-030-11726-9_6
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
Belfin, R.V., Anitha, J., Nainan, A., Thomas, L. (2022). An Efficient Approach for Brain Tumor Detection Using Deep Learning Techniques. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_25
Download citation
DOI: https://doi.org/10.1007/978-981-16-2594-7_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2593-0
Online ISBN: 978-981-16-2594-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)