Modified U-Net for automatic brain tumor regions segmentation
Kaewrak, Keerati and Soraghan, John and Di Caterina, Gaetano and Grose, Derek (2019) Modified U-Net for automatic brain tumor regions segmentation. In: 27th European Signal Processing Conference, 2019-09-02 - 2019-09-06.
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Abstract
Novel deep learning based network architectures are investigated for advanced brain tumor image classification and segmentation. Variations in brain tumor characteristics together with limited labelled datasets represent significant challenges in automatic brain tumor segmentation. In this paper, we present a novel architecture based on the U-Net that incorporates both global and local feature extraction paths to improve the segmentation accuracy. The results included in the paper show superior performance of the novel segmentation for five tumor regions on the large BRATs 2018 dataset over other approaches.
Creators(s): |
Kaewrak, Keerati, Soraghan, John ![]() ![]() | Item type: | Conference or Workshop Item(Paper) |
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ID code: | 69095 |
Keywords: | segmentation, u-net, tumor, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering |
Subjects: | Technology > Electrical engineering. Electronics Nuclear engineering |
Department: | Faculty of Engineering > Electronic and Electrical Engineering Technology and Innovation Centre > Sensors and Asset Management |
Depositing user: | Pure Administrator |
Date deposited: | 29 Jul 2019 10:47 |
Last modified: | 01 Jan 2021 07:52 |
Related URLs: | |
URI: | https://strathprints.strath.ac.uk/id/eprint/69095 |
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