Paper Title : Efficient Classification Of Brain Tumors Images Using Neural Network Technique

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6397160

Authors: R.Navin Kumar M.C.A.,M.Phil., S.Raveenthiran

         



MLA Style: Efficient Classification Of Brain Tumors Images Using Neural Network Technique " R.Navin Kumar M.C.A.,M.Phil., S.Raveenthiran " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Efficient Classification Of Brain Tumors Images Using Neural Network Technique " R.Navin Kumar M.C.A.,M.Phil., S.Raveenthiran " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Using biopsy, brain tumors classification is performed, which is not normally conducted before definitive brain surgery. The technology improvement and machine learning helps radiologists for diagnostics of tumor without invasive measures. Convolutional neural network (CNN) is the machine-learning algorithm which achieved substantial results in image classification and segmentation. Some of the most notable primary brain tumors are meningiomas, gliomas and pituitary tumors. Gliomas is a general term for tumor which arise from the brain tissues other than the nerve cells and the blood vessels. But, meningiomas arise from membranes that cover brain and surround central nervous system, whereas pituitary tumors are the lumps that sit inside skull. Most notable important difference between these three types is that meningiomas are generally benign, and gliomas are commonly malignant. This project deevlops a new CNN architecture to classify brain tumor types. With i) good generalization capability and ii) good execution speed, newly developed CNN architecture are being used as an effective decision-support tool for radiologists in diagnostics. Python is used for development of the project.

Reference
[1] World Health Organization—Cancer. Available online: https://www.who.int/news-room/fact-sheets/detail/ cancer. (accessed on 5 November 2019). [2] Priya, V.V. An Efficient Segmentation Approach for Brain Tumor Detection in MRI. Indian J. Sci. Technol. 2016, 9, 1–6. [3] Cancer Treatments Centers of America—Brain Cancer Types. Available online: https://www.cancercenter.com/cancer-types/braincancer/types (accessed on 30 November 2019). [4] American Association of Neurological Surgeons— Classification of Brain Tumours. Available online: https://www.aans.org/en/Media/Classifications-of-BrainTumors (accessed on 30 November 2019). [5] DeAngelis, L.M. Brain Tumors. New Engl. J. Med. 2001, 344, 114–123. [CrossRef] [6] Louis, D.N.; Perry, A.; Reifenberger, G.; Von Deimling, A.; Figarella-Branger, M.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [CrossRe [7] Afshar, P.; Plataniotis, K.N.; Mohammadi, A. Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries. In Proceedings of the ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May 2019; pp. 1368–1372. [8] Byrne, J.; Dwivedi, R.; Minks, D. Tumours of the brain. In Nicholson T (ed) Recommendations Cross Sectional Imaging Cancer Management, 2nd ed.; Royal College of Radiologists: London, UK, 2014; pp. 1–20. Available online: https://www.rcr.ac.uk/publication/recommendations-crosssectional-imaging-cancer-managementsecond-edition (accessed on 5 November 2019). [9] Center for Biomedical Image Computing & Analytics (CBICA). Available online: http://braintumorsegmentation.org/ (accessed on 5 November 2019). [10] Mlynarski, P.; Delingette, H.; Criminisi, A.; Ayache, N. Deep learning with mixed supervision for brain tumor segmentation. J. Med Imaging 2019, 6, 034002. [CrossRe [11] Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020 (https://gco.iarc.fr/today, accessed February 2021). [12] de Martel C, Georges D, Bray F, Ferlay J, Clifford GM. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis. Lancet Glob Health. 2020;8(2):e180-e190. [13] Assessing national capacity for the prevention and control of noncommunicable diseases: report of the 2019 global survey. Geneva: World Health Organization; 2020.

Keywords
— Deep Learning, Neural Network, Brain Tumor, MRI Images.