Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks
Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in del...
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Elsevier
2025-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990025000369 |
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| author | Wiley Tam Paul Babyn Javad Alirezaie |
| author_facet | Wiley Tam Paul Babyn Javad Alirezaie |
| author_sort | Wiley Tam |
| collection | DOAJ |
| description | Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in delayed diagnosis and treatment. In response, Computer-Aided Diagnostic (CAD) tools can be used to potentially streamline and expedite the diagnostic process. Recently, deep learning techniques have gained prominence in the automated analysis of CXR images, particularly in segmenting lung regions as a critical preliminary step. This study aims to develop and evaluate a lung segmentation model based on a modified U-Net architecture. The architecture leverages techniques such as transfer learning with DenseNet201 as a feature extractor alongside dilated convolutions and residual blocks. An ablation study was conducted to evaluate these architectural components, along with additional elements like augmented data, alternative backbones, and attention mechanisms. Numerous and extensive experiments were performed on two publicly available datasets, the Montgomery County (MC) and Shenzhen Hospital (SH) datasets, to validate the efficacy of these techniques on segmentation performance. Outperforming other state-of-the-art methods on the MC dataset, the proposed model achieved a Jaccard Index (IoU) of 97.77 and a Dice Similarity Coefficient (DSC) of 98.87. These results represent a significant improvement over the baseline U-Net, with gains of 3.37% and 1.75% in IoU and DSC, respectively. These findings highlight the importance of architectural enhancements in deep learning-based lung segmentation models, contributing to more efficient, accurate, and reliable CAD systems for lung disease assessment. |
| format | Article |
| id | doaj-art-602e567cb4c54e3fae6cc462aa281a09 |
| institution | Kabale University |
| issn | 2666-9900 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj-art-602e567cb4c54e3fae6cc462aa281a092025-08-20T03:40:41ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002025-01-01810021110.1016/j.cmpbup.2025.100211Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocksWiley Tam0Paul Babyn1Javad Alirezaie2Department of Electrical, Computer and Biomedical Eng., Toronto Metropolitan University, Toronto, M5B 2K3, ON, CanadaDepartment of Medical Imaging, University of Saskatchewan, Saskatoon, S7N 0W8, SK, CanadaDepartment of Electrical, Computer and Biomedical Eng., Toronto Metropolitan University, Toronto, M5B 2K3, ON, Canada; Corresponding author.Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in delayed diagnosis and treatment. In response, Computer-Aided Diagnostic (CAD) tools can be used to potentially streamline and expedite the diagnostic process. Recently, deep learning techniques have gained prominence in the automated analysis of CXR images, particularly in segmenting lung regions as a critical preliminary step. This study aims to develop and evaluate a lung segmentation model based on a modified U-Net architecture. The architecture leverages techniques such as transfer learning with DenseNet201 as a feature extractor alongside dilated convolutions and residual blocks. An ablation study was conducted to evaluate these architectural components, along with additional elements like augmented data, alternative backbones, and attention mechanisms. Numerous and extensive experiments were performed on two publicly available datasets, the Montgomery County (MC) and Shenzhen Hospital (SH) datasets, to validate the efficacy of these techniques on segmentation performance. Outperforming other state-of-the-art methods on the MC dataset, the proposed model achieved a Jaccard Index (IoU) of 97.77 and a Dice Similarity Coefficient (DSC) of 98.87. These results represent a significant improvement over the baseline U-Net, with gains of 3.37% and 1.75% in IoU and DSC, respectively. These findings highlight the importance of architectural enhancements in deep learning-based lung segmentation models, contributing to more efficient, accurate, and reliable CAD systems for lung disease assessment.http://www.sciencedirect.com/science/article/pii/S2666990025000369Lung segmentationDeep learningComputer aided diagnosticU-netResidual networks |
| spellingShingle | Wiley Tam Paul Babyn Javad Alirezaie Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks Computer Methods and Programs in Biomedicine Update Lung segmentation Deep learning Computer aided diagnostic U-net Residual networks |
| title | Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks |
| title_full | Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks |
| title_fullStr | Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks |
| title_full_unstemmed | Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks |
| title_short | Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks |
| title_sort | robust lung segmentation in chest x ray images using modified u net with deeper network and residual blocks |
| topic | Lung segmentation Deep learning Computer aided diagnostic U-net Residual networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666990025000369 |
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