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Application of multi- path CNN for brain tumor segmentation from MRI

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dc.contributor.author Chowdhury, Samia
dc.contributor.author Shaju, Sm Shahjalal
dc.date.accessioned 2019-07-18T05:05:42Z
dc.date.available 2019-07-18T05:05:42Z
dc.date.issued 2018-12-22
dc.identifier.uri http://hdl.handle.net/123456789/3005
dc.description.abstract The automation of brain tumor segmentation from MRI is an active topic in the field of medical research. Different approaches and methods are being proposed throughout the years to address this challenging task. The application of convolutional neural network has caught the attention of many researchers for the solution of this particular problem due to its extraordinary performances in the field of computer vision . Many of the state-of-the-art techniques use different approaches based on CNN. One of such approaches is the multi-path CNN architecture. In this paper, we propose a novel multi-path CNN architecture that allows flow of information in two different pathways resulting in the exploitation of both local and global features simultaneously, hence this architecture can also be called two-pathway architecture. We use this architecture to train on the dataset collected from BRATS 2013 challenge. Our model when tested on BRATS 2013 training images, showed on an average 95 .654% accuracy. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.relation.ispartofseries ;P12431
dc.subject Computer Science en_US
dc.subject Information Technology en_US
dc.subject Brain Tumor en_US
dc.subject Convolution Neural Network en_US
dc.title Application of multi- path CNN for brain tumor segmentation from MRI en_US
dc.type Thesis en_US

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