A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning
Abstract This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas’s (TCGA’s) lower-grade glioma collection...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01837-4 |
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| _version_ | 1849738579071205376 |
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| author | Amin Pourmahboubi Nazanin Arsalani Saeed Hamed Tabrizchi |
| author_facet | Amin Pourmahboubi Nazanin Arsalani Saeed Hamed Tabrizchi |
| author_sort | Amin Pourmahboubi |
| collection | DOAJ |
| description | Abstract This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas’s (TCGA’s) lower-grade glioma collection, our proposed approach uses a VGG19-based U-Net architecture with fixed pretrained weights. The experimental findings, which show an Area Under the Curve (AUC) of 0.9957, F1-Score of 0.9679, Dice Coefficient of 0.9679, Precision of 0.9541, Recall of 0.9821, and Intersection-over-Union (IoU) of 0.9378, show how effective the proposed framework is. According to these metrics, the VGG19-powered U-Net outperforms not only the conventional U-Net model but also other variants that were compared and used different pre-trained backbones in the U-Net encoder. Clinical trial registration Not applicable as this study utilized existing publicly available dataset and did not involve a clinical trial. |
| format | Article |
| id | doaj-art-998dc213b9334d67a2fd9cceadeda838 |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-998dc213b9334d67a2fd9cceadeda8382025-08-20T03:06:31ZengBMCBMC Medical Imaging1471-23422025-07-0125112410.1186/s12880-025-01837-4A brain tumor segmentation enhancement in MRI images using U-Net and transfer learningAmin Pourmahboubi0Nazanin Arsalani Saeed1Hamed Tabrizchi2Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of TabrizDepartment of Biology, Faculty of Natural Science, University of TabrizDepartment of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of TabrizAbstract This paper presents a novel transfer learning approach for segmenting brain tumors in Magnetic Resonance Imaging (MRI) images. Using Fluid-Attenuated Inversion Recovery (FLAIR) abnormality segmentation masks and MRI scans from The Cancer Genome Atlas’s (TCGA’s) lower-grade glioma collection, our proposed approach uses a VGG19-based U-Net architecture with fixed pretrained weights. The experimental findings, which show an Area Under the Curve (AUC) of 0.9957, F1-Score of 0.9679, Dice Coefficient of 0.9679, Precision of 0.9541, Recall of 0.9821, and Intersection-over-Union (IoU) of 0.9378, show how effective the proposed framework is. According to these metrics, the VGG19-powered U-Net outperforms not only the conventional U-Net model but also other variants that were compared and used different pre-trained backbones in the U-Net encoder. Clinical trial registration Not applicable as this study utilized existing publicly available dataset and did not involve a clinical trial.https://doi.org/10.1186/s12880-025-01837-4Brain tumor segmentationConvolutional neural networks (CNNs)Deep learningMagnetic resonance imaging (MRI)Medical image analysisNeuroimaging |
| spellingShingle | Amin Pourmahboubi Nazanin Arsalani Saeed Hamed Tabrizchi A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning BMC Medical Imaging Brain tumor segmentation Convolutional neural networks (CNNs) Deep learning Magnetic resonance imaging (MRI) Medical image analysis Neuroimaging |
| title | A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning |
| title_full | A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning |
| title_fullStr | A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning |
| title_full_unstemmed | A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning |
| title_short | A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning |
| title_sort | brain tumor segmentation enhancement in mri images using u net and transfer learning |
| topic | Brain tumor segmentation Convolutional neural networks (CNNs) Deep learning Magnetic resonance imaging (MRI) Medical image analysis Neuroimaging |
| url | https://doi.org/10.1186/s12880-025-01837-4 |
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