A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning

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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Bibliographic Details
Main Authors: Amin Pourmahboubi, Nazanin Arsalani Saeed, Hamed Tabrizchi
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Brain tumor segmentation
Convolutional neural networks (CNNs)
Deep learning
Magnetic resonance imaging (MRI)
Medical image analysis
Neuroimaging
Online Access:https://doi.org/10.1186/s12880-025-01837-4
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