Gliomas are considered as the most aggressive and commonly found type among brain tumors. This leads to the shortage of lives of oncological patients. These tumors are mostly by magnetic resonance imaging (MRI) from which the segmentation becomes a big problem because of the large structural and spatial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested on BRATS 2019 dataset. The average dice coefficient achieved is 0.9694.
Brain tumor segmentation using 2D-UNET convolutional neural network / Munir, Khushboo; Frezza, Fabrizio; Rizzi, Antonello. - (2021), pp. 239-248. - STUDIES IN COMPUTATIONAL INTELLIGENCE. [10.1007/978-981-15-6321-8_14].
Brain tumor segmentation using 2D-UNET convolutional neural network
Munir, Khushboo
;Frezza, Fabrizio;Rizzi, Antonello
2021
Abstract
Gliomas are considered as the most aggressive and commonly found type among brain tumors. This leads to the shortage of lives of oncological patients. These tumors are mostly by magnetic resonance imaging (MRI) from which the segmentation becomes a big problem because of the large structural and spatial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested on BRATS 2019 dataset. The average dice coefficient achieved is 0.9694.| File | Dimensione | Formato | |
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