2021
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Abstract: Purpose: Most state-of-the-art automated medical image analysis methods for volumetric data rely on adaptations of two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs). In this paper, we develop a novel unified CNN-based model that combines the benefits of 2D and 3D networks for analyzing volumetric medical images. Methods: In our proposed framework, multiscale contextual information is first extracted from 2D slices inside a volume of interest (VOI). This is followed by dilated… Show more

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Cited by 5 publications
(76 citation statements)
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References 32 publications
(76 reference statements)
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“…The median filter, which is a nonlinear filter, successfully retains cell edges in the images of breast cancer. The purpose of feature extraction is to enhance the overall classification and prediction performance [ 15 , 16 ]. The process involves producing prospective features using different transformation techniques.…”
Section: The Proposed Modelmentioning
confidence: 99%
“…The median filter, which is a nonlinear filter, successfully retains cell edges in the images of breast cancer. The purpose of feature extraction is to enhance the overall classification and prediction performance [ 15 , 16 ]. The process involves producing prospective features using different transformation techniques.…”
Section: The Proposed Modelmentioning
confidence: 99%