ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images
Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images h...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Computer Methods and Programs in Biomedicine Update |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990023000411 |
| _version_ | 1797357803025727488 |
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| author | Chukwuebuka Joseph Ejiyi Zhen Qin Ann O Nnani Fuhu Deng Thomas Ugochukwu Ejiyi Makuachukwu Bennedith Ejiyi Victor Kwaku Agbesi Olusola Bamisile |
| author_facet | Chukwuebuka Joseph Ejiyi Zhen Qin Ann O Nnani Fuhu Deng Thomas Ugochukwu Ejiyi Makuachukwu Bennedith Ejiyi Victor Kwaku Agbesi Olusola Bamisile |
| author_sort | Chukwuebuka Joseph Ejiyi |
| collection | DOAJ |
| description | Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals. |
| first_indexed | 2024-03-08T14:50:30Z |
| format | Article |
| id | doaj.art-c33dd64c91ef4c2293c24ab311bcaa78 |
| institution | Directory Open Access Journal |
| issn | 2666-9900 |
| language | English |
| last_indexed | 2024-03-08T14:50:30Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computer Methods and Programs in Biomedicine Update |
| spelling | doaj.art-c33dd64c91ef4c2293c24ab311bcaa782024-01-11T04:32:00ZengElsevierComputer Methods and Programs in Biomedicine Update2666-99002024-01-015100133ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray ImagesChukwuebuka Joseph Ejiyi0Zhen Qin1Ann O Nnani2Fuhu Deng3Thomas Ugochukwu Ejiyi4Makuachukwu Bennedith Ejiyi5Victor Kwaku Agbesi6Olusola Bamisile7Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, ChinaNetwork and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, ChinaCollege of Environmental Science and Engineering, Hohai University, Nanjing, ChinaNetwork and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China; Corresponding authorDepartment of Pure and Industrial Chemistry, University of Nigeria, Nsukka, Enugu, NigeriaPharmacy Department, University of Nigeria, Nsukka, Enugu, NigeriaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre Chengdu, University of Technology, Chengdu, ChinaPulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals.http://www.sciencedirect.com/science/article/pii/S2666990023000411Chest X-ray imagesExternal AttentionResfEANetResNetTuberculosis |
| spellingShingle | Chukwuebuka Joseph Ejiyi Zhen Qin Ann O Nnani Fuhu Deng Thomas Ugochukwu Ejiyi Makuachukwu Bennedith Ejiyi Victor Kwaku Agbesi Olusola Bamisile ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images Computer Methods and Programs in Biomedicine Update Chest X-ray images External Attention ResfEANet ResNet Tuberculosis |
| title | ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images |
| title_full | ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images |
| title_fullStr | ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images |
| title_full_unstemmed | ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images |
| title_short | ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images |
| title_sort | resfeanet resnet fused external attention network for tuberculosis diagnosis using chest x ray images |
| topic | Chest X-ray images External Attention ResfEANet ResNet Tuberculosis |
| url | http://www.sciencedirect.com/science/article/pii/S2666990023000411 |
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