Automated semantic lung segmentation in chest CT images using deep neural network
Article
Murugappan, M., Bourisly, Ali K., Prakash, N. B., Sumithra, M. G. and Acharya, U. Rajendra. 2023. "Automated semantic lung segmentation in chest CT images using deep neural network." Neural Computing and Applications. 35 (21), pp. 15343-15364. https://doi.org/10.1007/s00521-023-08407-1
| Article Title | Automated semantic lung segmentation in chest CT images using deep neural network |
|---|---|
| ERA Journal ID | 18089 |
| Article Category | Article |
| Authors | Murugappan, M., Bourisly, Ali K., Prakash, N. B., Sumithra, M. G. and Acharya, U. Rajendra |
| Journal Title | Neural Computing and Applications |
| Journal Citation | 35 (21), pp. 15343-15364 |
| Number of Pages | 22 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United Kingdom |
| ISSN | 0941-0643 |
| 1433-3058 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-023-08407-1 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s00521-023-08407-1 |
| Abstract | Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis. |
| Keywords | CoVID-19 |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Kuwait College of Science and Technology, Kuwait |
| Vels Institute of Science, Technology, and Advanced Studies, India | |
| University Malaysia Perlis, Malaysia | |
| Kuwait University, Kuwait | |
| National Engineering College, India | |
| Dr. N. G. P. Institute of Technology, India | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1vyq/automated-semantic-lung-segmentation-in-chest-ct-images-using-deep-neural-network
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