Mahyoub, M, Natalia, F, Sudirman, S, Jasim Al-Jumaily, AH and Liatsis, P (2023) Brain Tumor Segmentation in Fluid-Attenuated Inversion Recovery Brain MRI using Residual Network Deep Learning Architectures. In: Proceedings - 2023 15 International Conference on Developments in eSystems Engineering (DeSE) , 2023-J. pp. 486-491. (2023 15 International Conference on Developments in eSystems Engineering (DeSE), 09-12 January 2023, Baghdad & Anbar, Iraq).

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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)
Additional Information:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > T Technology (General)
Divisions:	Computer Science & Mathematics
Publisher:	IEEE
SWORD Depositor:	A Symplectic
Date Deposited:	12 Jun 2023 16:46
Last Modified:	12 Jun 2023 16:46
DOI or ID number:	10.1109/DeSE58274.2023.10100119
URI:	https://researchonline.ljmu.ac.uk/id/eprint/19757

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