Mahyoub, Mohamed and Natalia, Friska and Sudirman, Sud and Al- Jumaily, Abdulmajeed Hammadi Jasim and Liatsis, Panos (2023) Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE).
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Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures.pdf Download (2MB) | Preview |
Abstract
Early and accurate detection of brain tumors is very important to save the patient's life. Brain tumors are generally diagnosed manually by a radiologist by analyzing the patient’s brain MRI scans which is a time-consuming process. This led to our study of this research area for finding out a solution to automate the diagnosis to increase its speed and accuracy. In this study, we investigate the use of Residual Network deep learning architecture to diagnose and segment brain tumors. We proposed a two-step method involving a tumor detection stage, using ResNet50 architecture, and a tumor area segmentation stage using ResU-Net architecture. We adopt transfer learning on pre-trained models to help get the best performance out of the approach, as well as data augmentation to lessen the effect of data population imbalance and hyperparameter optimization to get the best set of training parameter values. Using a publicly available dataset as a testbed we show that our approach achieves 84.3% performance outperforming the state-of-the-art using U-Net by 2% using the Dice Coefficient metric.
| Item Type: | Conference or Workshop Item (Paper) |
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| Keywords: | Brain Tumor; Image Segmentation; Magnetic Resonance Imaging; Residual Networks; Deep Learning |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > 006.3 Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 07 Nov 2023 00:06 |
| Last Modified: | 07 Nov 2023 00:06 |
| URI: | https://kc.umn.ac.id/id/eprint/27047 |
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