Deep Neural Networks for Precise Brain Tumor Delineation: A U-Net and TensorFlow Approach
Received: 22 February 2025 | Revised: 20 March 2025 and 7 April 2025 | Accepted: 12 April 2025 | Online: 4 May 2025
Corresponding author: H. R. Divakar
Abstract
Brain tumors, especially gliomas, are complex and aggressive growths of cells in the brain that lead to high morbidity and mortality. With high-grade gliomas having a median survival rate of under two years, accurate and timely diagnosis is crucial. Magnetic Resonance Imaging (MRI) is the primary method for detecting brain tumors, but manual interpretation by radiologists can be time-consuming and subject to variability. Therefore, there is a growing need for more reliable and automated methods. This study proposes a deep learning approach for Brain Tumor Segmentation (BraTS) using the U-Net model in TensorFlow. U-Net is well-suited for biomedical image segmentation due to its encoder-decoder structure and skip connections, which capture detailed information and spatial context. The model is trained on the BraTS 2020 dataset, which includes MRI scans of high-grade and low-grade gliomas across four sequences: Fluid-Attenuated Inversion Recovery (FLAIR), T1-weighted, T1-weighted with Contrast Enhancement (T1CE), and T2-weighted. This work demonstrates the potential of deep learning to improve medical imaging precision, enhancing diagnosis and treatment planning for brain tumor patients.
Keywords:
brain tumor segmentation, gliomas, deep learning, U-net architectureDownloads
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Copyright (c) 2025 Niranjan C. Kundur, H. R. Divakar, Samitha Khaiyum, Kiran P. Rakshitha, Praveen M. Dhulavvagol, Anand S. Meti
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