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Title: Segmentation of Brain Tumor Using a 3D Generative Adversarial Network
Authors: Kalejahi, BK
Meshgini, S
Danishvar, S
Keywords: generative adversarial networks;brain tumor;medical image segmentation;computer aided diagnosis
Issue Date: 30-Oct-2023
Publisher: MDPI
Citation: Kalejahi, B.K., Meshgini, S. and Danishvar, S. (2023) 'Segmentation of Brain Tumor Using a 3D Generative Adversarial Network', Diagnostics, 2023, 13 (21), 3344, pp. 1 - 22. doi: 10.3390/diagnostics13213344.
Abstract: Copyright © 2023 by the authors. Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. The focus of this research is on the MRI images of the human brain, and an attempt has been made to propose a method for the accurate segmentation of these images to identify the correct location of tumors. In this study, GAN is utilized as a classification network to detect and segment of 3D MRI images. The 3D GAN network model provides dense connectivity, followed by rapid network convergence and improved information extraction. Mutual training in a generative adversarial network can bring the segmentation results closer to the labeled data to improve image segmentation. The BraTS 2021 dataset of 3D images was used to compare two experimental models.
Description: Data Availability Statement: Used dataset is available in: and prepared model is available in: accessed on 7 September 2023.
Other Identifiers: ORCID iD: Behnam Kiani Kalejahi
ORCID iD: Sebelan Danishvar
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers
Dept of Civil and Environmental Engineering Research Papers

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