Automatic Brain Tumor Segmentation: Advancing U-Net With ResNet50 Encoder for Precise Medical Image Analysis
Jagiellonian University · AGH University of Krakow · +2 more institutions
Abstract
Automated brain tumor segmentation from MRI images is critical for accurate diagnosis and treatment planning. This study presents a novel ResUNet50-based approach, integrating ResNet50 as an encoder within the U-Net framework to achieve robust and precise segmentation. The proposed model was evaluated on two datasets: a Kaggle-based T1-CE MRI dataset and BraTS 2018, ensuring comprehensive assessment across different imaging conditions. ResUNet50 outperformed state-of-the-art models, achieving Dice coefficients exceeding 0.95 and Jaccard indices above 0.91 on the Kaggle dataset. Additionally, experiments on BraTS 2018 Whole Tumor segmentation across multiple MRI modalities (FLAIR, T1, T1-CE, and T2)…
Citation impact
- FWCI
- 28.39
- Percentile
- 100%
- References
- 80
Authors
4Topics & keywords
- Computer science
- Image segmentation
- Artificial intelligence
- Computer vision
- Encoder
- Segmentation
- Medical imaging
- Image (mathematics)