Abstract
In the recent years, health issues have inescapably become center of attention for many researchers. Brain tumor is now a leading cause of death among medically certified deaths. Brain image diagnosis is manually examined by the neurologist. It is time consuming and may lead to errors. The general idea of this research is to analyze the brain tumor based on magnetic resonance imaging (MRI) of medical images. The design of this system is aimed at detecting the brain tumor classifying the MRI samples. The system uses computer-based procedures to detect tumor blocks and classify the type of tumor to normal, benign, and malignant using tensor flow in MRI images of different patients. A promising method to perform the design is through a deep learning process. Deep learning is currently a well-known and superior method in the pattern recognition field. The performance measure for detection would be Equal Error Rate (EER), false acceptance rate (FAR), and false rejection rate (FRR). The higher percentage of accuracy of the biometric system depended on how much lower the ERR value would be. The samples are already available from a standard database, Multimodal Brain Tumor Image Segmentation Benchmark (BraTS). A comparison had been done between two different methods for classification of the brain tumor.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ostrom QT et al (2016) CBTRUS statistical report: primary brain and other central nervous systems tumors diagnosed in United States in 2009–2013. Neuro Oncol 18:v1–v75
Amiri S, Rekik I, Mahjoub MA (2016) Deep random forest-based learning transfer to SVM for brain tumor segmentation. In: 2nd international conference on advanced technologies for signal and image processing, ATSIP 2016
Itqan K et al (2016) User identification system based on finger-vein patterns using Convolutional Neural Network. ARPN J Eng Appl Sci 11(5):3316–3319
Syafeeza A et al (2015) Convolutional neural networks with fused layers applied to face recognition. Int J Comput Intell Appl 14(03):1550014
Makde V et al (2018) Deep neural network based classification of tumourous and non-tumorous medical images. In: Smart innovation, systems and technologies, pp 199–206
Shen H, Zhang J, Zheng W (2017) Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. In: ICIP (2017, to appear) Google Scholar, 2017
Menze B et al (2015) Brain tumor segmentation with deep learning, Multimodal brain tumor image segmentation (BRATS) challenge. In: MICCAI
Acknowledgements
The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Higher Education for supporting this research under PJP/2018/FKEKK (9D)/S01622.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ali, N.A., Syafeeza, A.R., Geok, L.J., Wong, Y.C., Hamid, N.A., Jaafar, A.S. (2019). Design of Automated Computer-Aided Classification of Brain Tumor Using Deep Learning. In: Piuri, V., Balas, V., Borah, S., Syed Ahmad, S. (eds) Intelligent and Interactive Computing. Lecture Notes in Networks and Systems, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-13-6031-2_11
Download citation
DOI: https://doi.org/10.1007/978-981-13-6031-2_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6030-5
Online ISBN: 978-981-13-6031-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)