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- Title
Automatic Segmentation on Glioblastoma Brain Tumor Magnetic Resonance Imaging Using Modified U-Net.
- Authors
Tjahyaningtijas, Hapsari Peni Agustin; Nugroho, Andi Kurniawan; Angkoso, Cucun Very; Purnama, I. Ketut Eddy; Purnomo, Mauridhi Hery
- Abstract
Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable architecture for the automatic segmentation of glioblastoma brain tumors. The segmentation performance evaluated using three models of U-Net architecture. The first architecture is the original U-Net, second is U-Net with all dropout in each convolutional layer and third is mU-Net that modified from both original U-Net and all drop-out U-Net. Through experimental findings, the most fitting architectural model is mU-Net architecture with an epoch number of 90 and a drop out layer value of 0.5. The results of the segmentation performance are shown by a dice score value of 0.909 which is greater than that of the previous research.
- Subjects
BRAIN tumors; GLIOBLASTOMA multiforme; MAGNETIC resonance imaging; ARCHITECTURAL models; CANCER
- Publication
EMITTER International Journal of Engineering Technology, 2020, Vol 8, Issue 1, p161
- ISSN
2355-391X
- Publication type
Article
- DOI
10.24003/emitter.v8i1.505