Glioblastoma detection using vgg16, inceptionnet, alexnet, resnet,  and their comparative analysis

Naser, Homayed; Tuba, Sidratul Tamzida; Sanvir, Md. Shihan

dc.contributor.author	Naser, Homayed
dc.contributor.author	Tuba, Sidratul Tamzida
dc.contributor.author	Sanvir, Md. Shihan
dc.date.accessioned	2022-04-06T02:21:06Z
dc.date.available	2022-04-06T02:21:06Z
dc.date.issued	2021-03-30
dc.identifier.citation	[1] N. Saxena, R. Sharma, K. Joshi, and H. S. Rana, \Identi cation of glioma from mr images using convolutional neural network," in Proceedings of the Future Technologies Conference, pp. 589{597, Springer, 2018. [2] By Mayo Clinic Sta , \Glioma," 2020. [Online; accessed 4-April-2020]. [3] Bruce Vendt, \Tcga-gbm," 2014. [Online; accessed 16-Dec-2020]. [4] Tracy Nolan, \Lgg-1p19qdeletion," 2014. [Online; accessed 4-Feb-2021]. [5] Spyridon Bakas, Ph.D. , \Multimodal brain tumor segmentation challenge 2020: Data," 2020. [Online; accessed 4-Feb-2021]. [6] Wikipedia contributors, \Glioblastoma," 2020. [Online; accessed 5-Feb-2021]. [7] F. Hoseini, A. Shahbahrami, and P. Bayat, \An e cient implementation of deep convolutional neural networks for mri segmentation," Journal of digital imaging, vol. 31, no. 5, pp. 738{747, 2018. [8] M. Liu, J. Zhang, D. Nie, P.-T. Yap, and D. Shen, \Anatomical landmark based deep feature representation for mr images in brain disease diagnosis," IEEE journal of biomedical and health informatics, vol. 22, no. 5, pp. 1476{ 1485, 2018. [9] B. Devkota, A. Alsadoon, P. Prasad, A. Singh, and A. Elchouemi, \Image segmentation for early stage brain tumor detection using mathematical mor- 16 phological reconstruction," Procedia Computer Science, vol. 125, pp. 115{123, 2018. [10] E. I. Zacharaki, S. Wang, S. Chawla, D. Soo Yoo, R. Wolf, E. R. Melhem, and C. Davatzikos, \Classi cation of brain tumor type and grade using mri texture and shape in a machine learning scheme," Magnetic Resonance in Medicine: An O cial Journal of the International Society for Magnetic Resonance in Medicine, vol. 62, no. 6, pp. 1609{1618, 2009. [11] S. Banerjee, S. Mitra, F. Masulli, and S. Rovetta, \Deep radiomics for brain tumor detection and classi cation from multi-sequence mri," arXiv preprint arXiv:1903.09240, 2019. [12] S. Ortega, H. Fabelo, D. K. Iakovidis, A. Koulaouzidis, and G. M. Callico, \Use of hyperspectral/multispectral imaging in gastroenterology. shedding some{di erent{light into the dark," Journal of clinical medicine, vol. 8, no. 1, p. 36, 2019. [13] T. Kalaiselvi, T. Padmapriya, P. Sriramakrishnan, and V. Priyadharshini, \Development of automatic glioma brain tumor detection system using deep convolutional neural networks," International Journal of Imaging Systems and Technology, vol. 30, no. 4, pp. 926{938, 2020. [14] Y. Zhuge, H. Ning, P. Mathen, J. Y. Cheng, A. V. Krauze, K. Camphausen, and R. W. Miller, \Automated glioma grading on conventional mri images using deep convolutional neural networks," Medical physics, vol. 47, no. 7, pp. 3044{3053, 2020. [15] Wikipedia contributors, \Principal component analysis," 2020. [Online; ac- cessed 13-Feb-2021]. [16] Wikipedia contributors, \Keras resnet: Building, training scaling residual nets on keras," 2020. [Online; accessed 13-Feb-2021]. [17] Wikipedia contributors, \Convolutional neural networks are fantastic for vi- sual recognition tasks.," 2020. [Online; accessed 13-Feb-2021]. 17	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1301
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704	en_US
dc.description.abstract	In this study we propose a deep learning based model for the detection of glioblastoma(an aggressive type of cancer that can occur in the brain or spinal cord) based on their origin in brain . The deep learnig based model will be obtained from the comparative analysis of the mdoels resnet,vgg16 , inception- net,alexnet .We also attempt to overcome the shortcoming of the above paper( Identi cation of Glioma from MR Images Using Convolutional Neural Network ) [1] which is some astrocytomas and oligodendrocytomas are misidenti ed as GBM and do a comparative analysis between the implemented model.We conducted our experiment using the datasets TCGA and LGG1p19q Depletion from Cancerar- chieve,Medpix,BraTS2020 consisting of samples over more than 12,000 patients. The experiments using Glioma images from the Brats2020 shows that we obtain 86% average classi cation accuracy for the network .	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Glioblastoma, resnet,Vgg16 , Inceptionnet,Alexnet	en_US
dc.title	Glioblastoma detection using vgg16, inceptionnet, alexnet, resnet, and their comparative analysis	en_US
dc.type	Thesis	en_US