Brain Tumour Classification Using Convolutional Neural Networks

Authors

  • Jasbin Merly M  PG scholar, Department of Communication Systems, St. Xavier's Catholic College of Engineering, Chunkankadai, Nagercoil, Tamil Nadu 629003, India
  • Mrs. C. Sheeja Herobin Rani  Assistant Professor, Department of Electronics and Communication Engineering, St. Xavier's Catholic College of Engineering, Chunkankadai, Nagercoil, Tamil Nadu 629003, India

Keywords:

Brain extraction, MRI brain, Brain structure segmentation, CNN

Abstract

Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of disease. Especially, brain tumour diagnosis requires high accuracy, where minute errors in judgment may lead to misfortune. This project focuses on a solution to identify brain tumour using convolutional neural network. The classification mainly depends on segmentation and region estimation. The segmentation process mainly includes feature extraction by the use of convolutional layer and pooling layer. The pooling layer performs max pooling and average pooling. Segmentation includes training and testing. In segmentation, the intensities get standardized and then the non tumor regions are masked. CNN classifier is used for the classification of type of tumor in the brain. Region growing is used to locate the exact region where the tumor is present. The proposed method is easy to perform when compared to the manual segmentation.

References

  1. Z. Jia and D. Chen, "Brain Tumor Identification and Classification of MRI images using deep learning techniques," in IEEE Access, doi: 10.1109/ACCESS.2020.3016319.
  2. P. Mohamed Shakeel, T. E. E. Tobely, H. Al-Feel, G. Manogaran and S. Baskar, "Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor," in IEEE Access, vol. 7, pp. 5577-5588, 2019, doi: 10.1109/ACCESS.2018.2883957.
  3. G. A. Wright, "Magnetic resonance imaging," in IEEE Signal Processing Magazine, vol. 14, no. 1, pp. 56-66, Jan. 1997, doi: 10.1109/79.560324.
  4. Anshika Sharma, Shailendra Narayan Singh and Sushil Kumar, “An innovative approach based on skull stripping for MRI images of human brain”, 2017 2nd International Conference on Telecommunication and Networks (TEL-NET).
  5. A. Subudhi, J. Jena and S. Sabut, "Extraction of brain from MRI images by skull stripping using histogram partitioning with maximum entropy divergence," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, 2016, pp. 0931-0935, doi: 10.1109/ICCSP.2016.7754284.
  6. M. Chansuparp, A. Rodtook, S. Rasmequan and K. Chinnasarn, "The automated skull stripping of brain magnetic resonance images using the integrated method," 2015 8th Biomedical Engineering International Conference (BMEiCON), Pattaya, Thailand, 2015, pp. 1-5, doi: 10.1109/BMEiCON.2015.7399548.
  7. Karuppanagounder, Somasundaram & Shankar, Siva. (2014). Automated Skull Stripping Method using Clustering and Histogram Analysis for MRI Human Head Scans. International Journal of Advanced Research in Computer Science and Tecnology. Volume 2. 117-122.
  8. André G.R. Balan, Agma J.M. Traina, Marcela X. Ribeiro, Paulo M.A. Marques, Caetano Traina Jr., Smart histogram analysis applied to the skull- stripping problem in T1-weighted MRI, Computers in Biology and Medicine, Volume 42, Issue 5, 2012, Pages 509-522, ISSN 0010-4825.
  9. A.Yan-Li, "Introduction to Digital Image Pre-processing and Segmentation," 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, Nanchang, China, 2015, pp. 588-593, doi: 10.1109/ICMTMA.2015.148.
  10. L. Chen, D. Liu, X. Xi and H. Xie, "Image Preprocessing Algorithm Analysis Based on Robertsoperator and Connected Region," 2012 International Conference on Computer Science and Service System, Nanjing, China, 2012, pp. 1682-1686, doi: 10.1109/CSSS.2012.421.
  11. Muhammad Sharif, Javaria Amin, Mudassar Raza, Muhammad Almas Anjum, Humaira Afzal, Shafqat Ali Shad (2020), ‘Brain tumor detection based on extreme learning’, Neural Computing and Applications, Neural computing applications ,vol 32 ,pp.15975-15985.
  12. Munesh Chandra Trivedi, Paritosh Tripathi, Vineet Kumar Singh (2020), ‘Brain tumour segmentation in magnetic resonance imaging using OKM approach’, Materials Proceedings.
  13. Nagwa M. Aboelenein, Piao Song hao, Anis Koubaa, Alam Noor, and Ahmed Afifi (2020), ‘HTTU-Net : Hybrid Two Track U Net for Automatic Brain Tumor Segmentation’, IEEE transactions on Special Section On Emerging Deep Learning Theories And Methods For Biomedical Engineering, Vol 8.
  14. Neelum Noreen , Sellappan Palaniappan, Abdul Qayyum , Iftikhar Ahmad , Muhammad Imran , and Muhammad Shoaib ( 2020), ‘A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor’ IEEE transactions on Special Section On Scalable Deep Learning For Big Data, Vol 8.
  15. Ping Iu , Qi Dou , Qiong Wang , and Pheng-Ann Heng (2020), ‘An Encoder-Decoder Neural Network With 3D Squeeze-and-Excitation and Deep Supervision for Brain Tumor Segmentation’, IEEE access, vol 8 .
  16. Pitchai .R, P. Supraja, A. Helen Victoria, M. Madhavi (2020), ‘Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images’, Neural processing letters.
  17. Rajan P.G and C. Sundar (2019), ‘Brain Tumor Detection and Segmentation by Intensity Adjustment’, Journal of Medical Systems.
  18. Ramy A. Zeineldin, Mohamed E. Karar, Jan Coburger, Christian R. Wirtz, Oliver Burgert (2020), ‘Deep Seg: deep neural network frame work for automatic brain tumor segmentation using magnetic resonance FLAIR images’, International Journal of Computer Assisted Radiology and Surgery, vol 15.
  19. Salma Alqazzaz, Xianfang Sun, Xin Yang, and Len Nokes( 2019), ‘Atomated brain tumor segmentation on multi-modal MR image using Seg Net’, Research article, Computational visual media, Vol. 5, No. 2, 209–219.
  20. Umit Ilhana, Ahmet Ilhan (2017), ’Brain tumor segmentation based on a new threshold approach’, 9th International Conference on Theory and application of Soft Computing, 580–587.
  21. Wu Deng , Qinke Shi , Miye Wang , Bing Zheng , And Ning Ning (2020), ‘Deep Learning-Based HCNN and CRF-RRNN Model for Brain Tumor Segmentation’ ,IEEE access,vol 8 , pp.26665-26675.
  22. Yi Ding , Fujuan Chen , Yang Zhao , Zhixing Wu, Chao Zhang, and Dongyuan Wu (2019), ‘A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmentation’, IEEE transactions on Special Section On Deep Learning: Security And Forensics Research Advances And Challenges, vol 7,pp.104011-104024.
  23. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir, Farman Ali, Zakir Ali, Saeed Ahmed, and Jianfeng Lu ( 2019), ‘Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning’, Conference on National Key Research and Development, Vol 7,pp.17809-17822.
  24. Zheshu Jia, Deyun Chen ( 2019), ‘ Brain Tumor Identification and Classification of MRI images using deep learning techniques’, IEEE access, Vol 10.
  25. Zhiguan Huang, Xiaohao Du, Liangming Chen , Yuhe Li, Mei Liu, Yao Chou, And Long Jin ( 2020), ‘Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function’, IEEE access, Vol 8, pp.89281-89290.

International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

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How to Cite

[1]
Jasbin Merly M, Mrs. C. Sheeja Herobin Rani, " Brain Tumour Classification Using Convolutional Neural Networks, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.487-495, March-April-2021.