Digital Repository

MRI based Glioma segmentation using Deep Learning algorithms

Show simple item record

dc.contributor.author Kaldera, H. N. T. K.
dc.contributor.author Gunasekara, S. R.
dc.contributor.author Dissanayake, M. B.
dc.date.accessioned 2019-05-10T06:18:02Z
dc.date.available 2019-05-10T06:18:02Z
dc.date.issued 2019
dc.identifier.citation Kaldera, H. N. T. K., Gunasekara, S. R. and Dissanayake, M. B. (2019). MRI based Glioma segmentation using Deep Learning algorithms. IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka.P.51 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/20154
dc.description.abstract Primary brain tumors can be malignant (cancerous) or benign (non-cancerous). Out of primary brain tumors, gliomas are the most common and, high grade gliomas carry a poor prognosis. In our paper, we present a technique to segment the glioma cells in Magnetic Resonance Imaging (MRI) using faster Region based Convolutional Neural Network (R-CNN) and edge detection techniques in image processing algorithms. This study identifies the region of interest that is glioma cells, with higher confidence level and localize the tumor on the MRI with the tumor mask. Further, analysis shows that with the proposed technique it is possible to achieve an average detection accuracy, sensitivity, Dice score and confidence level of 99.81%, 87.72%, 91.14% and 93.6% respectively en_US
dc.language.iso en en_US
dc.publisher IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka en_US
dc.subject Magnetic Resonance Imaging en_US
dc.subject Region based Convolutional Neural Network en_US
dc.subject Glioma segmentation en_US
dc.subject deep Learning en_US
dc.title MRI based Glioma segmentation using Deep Learning algorithms en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Digital Repository


Browse

My Account