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http://bura.brunel.ac.uk/handle/2438/28394
Title: | UNet and Variants for Medical Image Segmentation |
Authors: | Ehab, W Huang, L Li, Y |
Keywords: | medical imaging;segmentation;performance analysis;UNet;Res-UNet;Attention Res-UNet |
Issue Date: | 26-Jun-2024 |
Publisher: | Scilight Press |
Citation: | Ehab, W., Huang, L. and Li, Y. (2024) 'UNet and Variants for Medical Image Segmentation', International Journal of Network Dynamics and Intelligence, 3 (2), 100009, pp. 1 - 23. doi: 10.53941/ijndi.2024.100009. |
Abstract: | Medical imaging plays a crucial role in modern healthcare by providing non-invasive visualisation of internal structures and abnormalities, enabling early disease detection, accurate diagnosis, and treatment planning. This study aims to explore the application of deep learning models, particularly focusing on the UNet architecture and its variants, in medical image segmentation. We seek to evaluate the performance of these models across various challenging medical image segmentation tasks, addressing issues such as image normalization, resizing, architecture choices, loss function design, and hyperparameter tuning. The findings reveal that the standard UNet, when extended with a deep network layer, is a proficient medical image segmentation model, while the Res-UNet and Attention Res-UNet architectures demonstrate smoother convergence and superior performance, particularly when handling fine image details. The study also addresses the challenge of high class imbalance through careful preprocessing and loss function definitions. We anticipate that the results of this study will provide useful insights for researchers seeking to apply these models to new medical imaging problems and offer guidance and best practices for their implementation. |
Description: | Data Availability Statement: Not applicable. |
URI: | https://bura.brunel.ac.uk/handle/2438/28394 |
DOI: | https://doi.org/10.53941/ijndi.2024.100009 |
Other Identifiers: | ORCiD: Yongmin Li https://orcid.org/0000-0003-1668-2440 100009 |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2024 by the authors. Creative Commons License. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). | 3.25 MB | Adobe PDF | View/Open |
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