Glioblastoma detection using vgg16, inceptionnet, alexnet, resnet, and their comparative analysis
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Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh
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 .
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
Keywords
Glioblastoma, resnet,Vgg16 , Inceptionnet,Alexnet
Citation
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