Automatic segmentation of chest X-ray images via deep-improved various U-Net techniques
Objectives Accurate segmentation of medical images is vital for effective disease diagnosis and treatment planning. This is especially important in resource-constrained environments. This study aimed to evaluate the performance of various U-Net-based deep learning architectures for chest X-ray (CXR)...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-08-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251366855 |
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| _version_ | 1850036535807705088 |
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| author | Sedat Orenc Mehmet Sirac Ozerdem Emrullah Acar Musa Yilmaz |
| author_facet | Sedat Orenc Mehmet Sirac Ozerdem Emrullah Acar Musa Yilmaz |
| author_sort | Sedat Orenc |
| collection | DOAJ |
| description | Objectives Accurate segmentation of medical images is vital for effective disease diagnosis and treatment planning. This is especially important in resource-constrained environments. This study aimed to evaluate the performance of various U-Net-based deep learning architectures for chest X-ray (CXR) segmentation and identify the most effective model in terms of both accuracy and computational efficiency. Methods We assessed the segmentation performance of eight U-Net variants: U-Net7, U-Net9, U-Net11, U-Net13, U-Net16, U-Net32, U-Net64, and U-Net128. The evaluation was conducted using a publicly available CXR dataset categorized into normal, COVID-19, and viral pneumonia classes. Each image was paired with a corresponding segmentation mask. Image preprocessing involved resizing, noise filtering, and normalization to standardize input quality. All models were trained under identical experimental conditions to ensure a fair comparison. Performance was evaluated using two key metrics: Intersection over Union (IoU) and Dice Coefficient (DC). Additionally, computational efficiency was measured by comparing the total number of trainable parameters and the training time for each model. Results U-Net9 achieved the highest performance among all tested models. It recorded a DC of 0.98 and an IoU of 0.96, outperforming both shallower and deeper U-Net architectures. Models with increased depth or filter width, such as U-Net128, showed diminishing returns in accuracy. These models also incurred significantly higher computational costs. In contrast, U-Net16 and U-Net32 demonstrated reduced segmentation accuracy compared to U-Net9. Overall, U-Net9 provided the optimal balance between precision and computational efficiency for CXR segmentation tasks. Conclusion The U-Net9 architecture offers a superior solution for CXR image segmentation. It combines high segmentation accuracy with computational practicality, making it suitable for real-world applications. Its implementation can support radiologists by enabling faster and more reliable diagnoses. This can lead to improved clinical decision-making and reduced diagnostic delays. Future work will focus on integrating U-Net9 with multimodal imaging data, such as combining CXR with computerized tomography or MRI scans. Additionally, exploration of advanced architectures, including attention mechanisms and hybrid models, is planned to further enhance segmentation performance. |
| format | Article |
| id | doaj-art-425a9e5fcb1b47ccafdbbf2ffeda54c3 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-425a9e5fcb1b47ccafdbbf2ffeda54c32025-08-20T02:57:07ZengSAGE PublishingDigital Health2055-20762025-08-011110.1177/20552076251366855Automatic segmentation of chest X-ray images via deep-improved various U-Net techniquesSedat Orenc0Mehmet Sirac Ozerdem1Emrullah Acar2Musa Yilmaz3 Electrical-Electronics Engineering Department, , Batman, Turkey Electrical-Electronics Engineering Department, , Diyarbakır, Turkey Electrical-Electronics Engineering Department, , Batman, Turkey Center for Environmental Research and Technology, , Riverside, CA, USAObjectives Accurate segmentation of medical images is vital for effective disease diagnosis and treatment planning. This is especially important in resource-constrained environments. This study aimed to evaluate the performance of various U-Net-based deep learning architectures for chest X-ray (CXR) segmentation and identify the most effective model in terms of both accuracy and computational efficiency. Methods We assessed the segmentation performance of eight U-Net variants: U-Net7, U-Net9, U-Net11, U-Net13, U-Net16, U-Net32, U-Net64, and U-Net128. The evaluation was conducted using a publicly available CXR dataset categorized into normal, COVID-19, and viral pneumonia classes. Each image was paired with a corresponding segmentation mask. Image preprocessing involved resizing, noise filtering, and normalization to standardize input quality. All models were trained under identical experimental conditions to ensure a fair comparison. Performance was evaluated using two key metrics: Intersection over Union (IoU) and Dice Coefficient (DC). Additionally, computational efficiency was measured by comparing the total number of trainable parameters and the training time for each model. Results U-Net9 achieved the highest performance among all tested models. It recorded a DC of 0.98 and an IoU of 0.96, outperforming both shallower and deeper U-Net architectures. Models with increased depth or filter width, such as U-Net128, showed diminishing returns in accuracy. These models also incurred significantly higher computational costs. In contrast, U-Net16 and U-Net32 demonstrated reduced segmentation accuracy compared to U-Net9. Overall, U-Net9 provided the optimal balance between precision and computational efficiency for CXR segmentation tasks. Conclusion The U-Net9 architecture offers a superior solution for CXR image segmentation. It combines high segmentation accuracy with computational practicality, making it suitable for real-world applications. Its implementation can support radiologists by enabling faster and more reliable diagnoses. This can lead to improved clinical decision-making and reduced diagnostic delays. Future work will focus on integrating U-Net9 with multimodal imaging data, such as combining CXR with computerized tomography or MRI scans. Additionally, exploration of advanced architectures, including attention mechanisms and hybrid models, is planned to further enhance segmentation performance.https://doi.org/10.1177/20552076251366855 |
| spellingShingle | Sedat Orenc Mehmet Sirac Ozerdem Emrullah Acar Musa Yilmaz Automatic segmentation of chest X-ray images via deep-improved various U-Net techniques Digital Health |
| title | Automatic segmentation of chest X-ray images via deep-improved various
U-Net techniques |
| title_full | Automatic segmentation of chest X-ray images via deep-improved various
U-Net techniques |
| title_fullStr | Automatic segmentation of chest X-ray images via deep-improved various
U-Net techniques |
| title_full_unstemmed | Automatic segmentation of chest X-ray images via deep-improved various
U-Net techniques |
| title_short | Automatic segmentation of chest X-ray images via deep-improved various
U-Net techniques |
| title_sort | automatic segmentation of chest x ray images via deep improved various u net techniques |
| url | https://doi.org/10.1177/20552076251366855 |
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