2022
DOI: 10.2352/j.imagingsci.technol.2022.66.6.060502
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Brain Tumor Segmentation using Automatic 3D Multi-channel Feature Selection Convolutional Neural Network

Abstract: Brain tumor segmentation is an important topic in medical image processing. Three-dimensional (3D) convolutional neural networks (CNN) make full use of feature information and have better segmentation performance. However, conventional 3D CNNs involve a significant number of parameters and has high hardware requirements, which is not conducive to clinical application. To solve these issues, this study proposes a lightweight highly-efficient 3D CNN for brain tumor magnetic resonance imaging segmentation. We fir… Show more

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Cited by 4 publications
(2 citation statements)
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“…Shan et al [48] proposed 3D CNN based on U-net architecture. Their model comprised three main units: improved depth-wise convolution (IDWC) unit which uses separable convolution instead of conventional convolution to extract feature maps and computationally saving resources.…”
Section: Encoder-decoder Architecturementioning
confidence: 99%
“…Shan et al [48] proposed 3D CNN based on U-net architecture. Their model comprised three main units: improved depth-wise convolution (IDWC) unit which uses separable convolution instead of conventional convolution to extract feature maps and computationally saving resources.…”
Section: Encoder-decoder Architecturementioning
confidence: 99%
“…Das et al [ 13 ] used 3D CNN in a cascaded format to extract whole tumors first in a series followed by the core tumor and then the enhanced core tumor. Shan et al [ 14 ] proposed a lightweight 3D CNN with improved depth and used multi-channel convolution kernels of different sizes to aggregate features. Ramin et al [ 15 ] used a cascade CNN to speed up the learning.…”
Section: Introductionmentioning
confidence: 99%