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Research Article
BRAIN TUMOUR IDENTIFICATION USING VGG-16
Sowmiya S R1
Maheshwaran D2
Karthickraja K3
Manikandan S4
Dinesh S5
12345 Dhanalakshmi Srinivasan Engineering college, Perambalur, Anna University, India.
Published Online: January-February 2022
Pages: 50-52
Cite this article
No DOIReferences
1. L.Guo,L.Zhao,Y.Wu,Y.Li,G.Xu,andQ.Yan,“Tumordetection in MR images using oneclass immune feature weighted SVMs,” IEEE Transactions on Magnetics, vol. 47, no. 10, pp. 3849–3852,2011.
2.R.Kumari,“SVMclassificationanapproachondetectingabnormalityinbrainMRIimages,”InternationalJournalofEngineeringResearchandApplications,vol.3,pp.1686–1690,2013.
3. DICOM Samples Image Sets, http://www.osirix-viewer.com/.
4. “Brainweb:SimulatedBrainDatabase” http://brainweb.bic.mni.mcgill.ca/cgi/brainweb1. For papers published in translation journals, please give the English citation first, followed by the original foreign-language citation .
5. Brain, Other CNS and Intracranial TumoursStatistics.Accessed: May 2019.
6. Bogowiczet al.M, “Post-radiochemotherapy PET radiomics in head and neck cancer—The influence of radiomics implementation on the reproducibility of local control tumor models” Radiotherapy Oncol., vol. 125, no. 3, pp. 385–391,2017.
7. Chang S.G, Bin Yu, Vetterli.Met al., “Adaptive wavelet thresholding for image denoising and compression ’’ Proc. IEEE, vol. 9, Sep. 2000.
8. Chen Yu; Chen Dian-ren; Li Yang; Chen Lei et al., “Otsu's thresholding method based on gray level-gradient two-dimensional histogram” 2010.
9. Nilesh Bhaskarrao Bahadure, A.K. (2017, March6). Retrieved from https://www.hindawi.com/journals/ijbi/2017/9749108/.
10. S. Mohsin, S. Sajjad, Z. Malik, and A. H. Abdullah, “Efficient way of skull stripping in MRI to detect brain tumor by applying morphological operations, after detection of false background,” International Journal of Information and Education Technology, vol. 2, no. 4, pp. 335–337, 2012.
11. Gavale, P. M., Aher, P. V., & Wani, D. V. (2017, April 4). Retrieved from https://www.irjet.net/archives/V4/i4/IRJET-V4I462.pdf.
12. N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation,”Magnetic Resonance Imaging, vol. 31, no. 8, pp. 1426–1438, 2013.
13. Samantaray, M.(2016, November 3). Retrieved from http://ieeexplore.ieee.org/document/7727089/
14. Nandi, A. (2016, April 11) Retrieved from http://ieeexplore.ieee.org/document/7449892/
15. C. C. Benson and V. L. Lajish, “Morphology based enhancement and skull stripping of MRI brain images,” in Proceedings of the international Conference on Intelligent Computing Applications (ICICA ’14), pp. 254–257, Tamilnadu, India, March 2014.
2.R.Kumari,“SVMclassificationanapproachondetectingabnormalityinbrainMRIimages,”InternationalJournalofEngineeringResearchandApplications,vol.3,pp.1686–1690,2013.
3. DICOM Samples Image Sets, http://www.osirix-viewer.com/.
4. “Brainweb:SimulatedBrainDatabase” http://brainweb.bic.mni.mcgill.ca/cgi/brainweb1. For papers published in translation journals, please give the English citation first, followed by the original foreign-language citation .
5. Brain, Other CNS and Intracranial TumoursStatistics.Accessed: May 2019.
6. Bogowiczet al.M, “Post-radiochemotherapy PET radiomics in head and neck cancer—The influence of radiomics implementation on the reproducibility of local control tumor models” Radiotherapy Oncol., vol. 125, no. 3, pp. 385–391,2017.
7. Chang S.G, Bin Yu, Vetterli.Met al., “Adaptive wavelet thresholding for image denoising and compression ’’ Proc. IEEE, vol. 9, Sep. 2000.
8. Chen Yu; Chen Dian-ren; Li Yang; Chen Lei et al., “Otsu's thresholding method based on gray level-gradient two-dimensional histogram” 2010.
9. Nilesh Bhaskarrao Bahadure, A.K. (2017, March6). Retrieved from https://www.hindawi.com/journals/ijbi/2017/9749108/.
10. S. Mohsin, S. Sajjad, Z. Malik, and A. H. Abdullah, “Efficient way of skull stripping in MRI to detect brain tumor by applying morphological operations, after detection of false background,” International Journal of Information and Education Technology, vol. 2, no. 4, pp. 335–337, 2012.
11. Gavale, P. M., Aher, P. V., & Wani, D. V. (2017, April 4). Retrieved from https://www.irjet.net/archives/V4/i4/IRJET-V4I462.pdf.
12. N. Gordillo, E. Montseny, and P. Sobrevilla, “State of the art survey on MRI brain tumor segmentation,”Magnetic Resonance Imaging, vol. 31, no. 8, pp. 1426–1438, 2013.
13. Samantaray, M.(2016, November 3). Retrieved from http://ieeexplore.ieee.org/document/7727089/
14. Nandi, A. (2016, April 11) Retrieved from http://ieeexplore.ieee.org/document/7449892/
15. C. C. Benson and V. L. Lajish, “Morphology based enhancement and skull stripping of MRI brain images,” in Proceedings of the international Conference on Intelligent Computing Applications (ICICA ’14), pp. 254–257, Tamilnadu, India, March 2014.
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