Effective lung nodule detection using deep CNN with dual attention mechanisms
Abstract Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51833-x |
| _version_ | 1797274601939533824 |
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| author | Zia UrRehman Yan Qiang Long Wang Yiwei Shi Qianqian Yang Saeed Ullah Khattak Rukhma Aftab Juanjuan Zhao |
| author_facet | Zia UrRehman Yan Qiang Long Wang Yiwei Shi Qianqian Yang Saeed Ullah Khattak Rukhma Aftab Juanjuan Zhao |
| author_sort | Zia UrRehman |
| collection | DOAJ |
| description | Abstract Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks. |
| first_indexed | 2024-03-07T15:00:42Z |
| format | Article |
| id | doaj.art-b46afe0cecc745358413e4abdf803ddb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:00:42Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b46afe0cecc745358413e4abdf803ddb2024-03-05T19:11:07ZengNature PortfolioScientific Reports2045-23222024-02-0114111210.1038/s41598-024-51833-xEffective lung nodule detection using deep CNN with dual attention mechanismsZia UrRehman0Yan Qiang1Long Wang2Yiwei Shi3Qianqian Yang4Saeed Ullah Khattak5Rukhma Aftab6Juanjuan Zhao7College of Computer Science and Technology (College of Data Science), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Data Science), Taiyuan University of TechnologyJinzhong College of InformationNHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical UniversityJinzhong College of InformationCentre of Biotechnology and Microbiology, University of PeshawarCollege of Computer Science and Technology (College of Data Science), Taiyuan University of TechnologyCollege of Computer Science and Technology (College of Data Science), Taiyuan University of TechnologyAbstract Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.https://doi.org/10.1038/s41598-024-51833-x |
| spellingShingle | Zia UrRehman Yan Qiang Long Wang Yiwei Shi Qianqian Yang Saeed Ullah Khattak Rukhma Aftab Juanjuan Zhao Effective lung nodule detection using deep CNN with dual attention mechanisms Scientific Reports |
| title | Effective lung nodule detection using deep CNN with dual attention mechanisms |
| title_full | Effective lung nodule detection using deep CNN with dual attention mechanisms |
| title_fullStr | Effective lung nodule detection using deep CNN with dual attention mechanisms |
| title_full_unstemmed | Effective lung nodule detection using deep CNN with dual attention mechanisms |
| title_short | Effective lung nodule detection using deep CNN with dual attention mechanisms |
| title_sort | effective lung nodule detection using deep cnn with dual attention mechanisms |
| url | https://doi.org/10.1038/s41598-024-51833-x |
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