Abstract
Brain tumor identification is an essential task for assessing the tumors and its classification based on the size of tumor. There are various types of imaging strategies such as X-rays, MRI, CT-scan used to recognize brain tumors. Computed Tomography (CT) scan images are used for in this work for Brain tumor Image Identification. CT-scan images are used, because as it gives size, shape and blood vessels detailing and is non-invasive technique. CT-scan is commonly utilized because of the superior quality of image. Deep learning (DL) is the most recent technology which gives higher efficiency results in recognition, classification. In this paper, the model is developed by using Convolution neural network to detect the tumor of brain image from a dataset from Kaggle. The dataset contains near about 1000 images. Tumor is identified by image processing algorithm using CNN, time complexity is 90 m sec, and the accuracy of the present system is 97.87%.
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
Similar content being viewed by others
References
DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123
Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. Special section on Deep Learning for computer Aided diagnosis. IEEE Access 7. https://doi.org/10.1109/access.2019.2919122
Stewart BW, Wild CP (2014) World cancer report 2014. IARC, Lyon, France
Brain, Other CNS and Intra cranial Tumors Statistics. Accessed: May 2019. [Online]. Available: https://www.cancerresearchuk.org/
Hunnur SS, Raut A, Kulkarni S (2017) Implementation of image processing for detection of brain tumors. In: 2017 international conference on computing methodologies and communication (ICCMC), Erode, pp 717–722. https://doi.org/10.1109/iccmc.2017.8282559
Gurbină M, Lascu M, Lascu D (2019) Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines. In: 42nd international conference on telecommunications and signal processing (TSP), Budapest, Hungary, pp 505–508. https://doi.org/10.1109/tsp.2019.8769040
Parveen, Singh A (2015) Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. In: 2015 2nd international conference on signal processing a integrated networks (SPIN), Noida, pp 98–102. https://doi.org/10.1109/spin.2015.7095308
Behin A, Hoang-Xuan K, Carpentier AF, Delattre J-Y (2003) Primary brain tumors in adults. Lancet 361(9354):323–331
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Gooden berger ML, Jenkins RB (2012) Genetic so fadult glioma. Cancer Genet 205(12):613–621
Drevelegas A (2002) Imaging of brain tumors with histological correlations. Springer, Berlin, Germany
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820
Bishop C (2006) Pattern recognition and machine learning. Springer-Verlag, Berlin, Germany
Machhale K, Nandpuru HB, Kapur V, Kosta L (2015) MRI brain cancer classification using hybrid classifier (SVM-KNN). In: Proceedings of international conference on industrial instrumentation and control (ICIC), pp 60–65
Shasidhar M, Raja VS, Kumar BV (2011) MRI brain image segmentation using modified fuzzy C-means clustering algorithm. In: Proceedings of international conference on communication systems and network technologies (CSNT), pp 473–478
Goswamiand S, Bhaiya LKP (2013) Brain tumour detection using unsupervised learning based neural network. In Proceedings of international conference on communication systems and network technologies (CSNT), pp 573–577
Hossain T, Shishir FS, Ashraf M, Al Nasim MDA, Shah FM (2019) Brain tumor detection using convolutional neural network. In: 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT)
Siar M, Teshnehlab M (2019) Brain tumor detection using deep neural network and machine learning algorithm. In: 2019 9th international conference on computer and knowledge engineering (ICCKE), Mashhad, Iran, 2019, pp 363–368. https://doi.org/10.1109/iccke48569.2019.8964846
Paul J (2017) Deep learning for brain tumor classification. In: 13 March 2017 SPIE 10137, Medical imaging 2017: biomedical applications in molecular, structural, and functional imaging, 1013710. doi: https://doi.org/10.1117/12.2254195
Seetha J, Selva kumar Raja S (2018) Brain tumor classification using convolutional neural networks. Biomed Pharmacol J 11:1457–1461. https://doi.org/10.13005/bpj/1511
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
More, R.B., Bhisikar, S.A. (2021). Brain Tumor Detection Using Deep Neural Network. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-69921-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69920-8
Online ISBN: 978-3-030-69921-5
eBook Packages: EngineeringEngineering (R0)