TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images
<p dir="ltr">Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most po...
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2024
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| _version_ | 1864513544640266240 |
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| author | Tawsifur Rahman (14150523) |
| author2 | Amith Khandakar (14151981) Ashiqur Rahman (3969347) Susu M. Zughaier (14151987) Muna Al Maslamani (12501671) Moajjem Hossain Chowdhury (16888830) Anas M. Tahir (16870077) Md. Sakib Abrar Hossain (21734561) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author |
| author_facet | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Ashiqur Rahman (3969347) Susu M. Zughaier (14151987) Muna Al Maslamani (12501671) Moajjem Hossain Chowdhury (16888830) Anas M. Tahir (16870077) Md. Sakib Abrar Hossain (21734561) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Ashiqur Rahman (3969347) Susu M. Zughaier (14151987) Muna Al Maslamani (12501671) Moajjem Hossain Chowdhury (16888830) Anas M. Tahir (16870077) Md. Sakib Abrar Hossain (21734561) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2024-02-17T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s12559-024-10259-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/TB-CXRNet_Tuberculosis_and_Drug-Resistant_Tuberculosis_Detection_Technique_Using_Chest_X-ray_Images/29590349 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Pharmacology and pharmaceutical sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Image processing Drug-resistant TB Deep learning Lung segmentation Tuberculosis detection |
| dc.title.none.fl_str_mv | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread.</p><h2>Other Information</h2><p dir="ltr">Published in: Cognitive Computation<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12559-024-10259-3" target="_blank">https://dx.doi.org/10.1007/s12559-024-10259-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f27b32a2ed4de655a9d91f5e6db6bf8a |
| identifier_str_mv | 10.1007/s12559-024-10259-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29590349 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray ImagesTawsifur Rahman (14150523)Amith Khandakar (14151981)Ashiqur Rahman (3969347)Susu M. Zughaier (14151987)Muna Al Maslamani (12501671)Moajjem Hossain Chowdhury (16888830)Anas M. Tahir (16870077)Md. Sakib Abrar Hossain (21734561)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesClinical sciencesPharmacology and pharmaceutical sciencesEngineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceImage processingDrug-resistant TBDeep learningLung segmentationTuberculosis detection<p dir="ltr">Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread.</p><h2>Other Information</h2><p dir="ltr">Published in: Cognitive Computation<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12559-024-10259-3" target="_blank">https://dx.doi.org/10.1007/s12559-024-10259-3</a></p>2024-02-17T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s12559-024-10259-3https://figshare.com/articles/journal_contribution/TB-CXRNet_Tuberculosis_and_Drug-Resistant_Tuberculosis_Detection_Technique_Using_Chest_X-ray_Images/29590349CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295903492024-02-17T09:00:00Z |
| spellingShingle | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images Tawsifur Rahman (14150523) Biomedical and clinical sciences Clinical sciences Pharmacology and pharmaceutical sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Image processing Drug-resistant TB Deep learning Lung segmentation Tuberculosis detection |
| status_str | publishedVersion |
| title | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| title_full | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| title_fullStr | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| title_full_unstemmed | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| title_short | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| title_sort | TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images |
| topic | Biomedical and clinical sciences Clinical sciences Pharmacology and pharmaceutical sciences Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Image processing Drug-resistant TB Deep learning Lung segmentation Tuberculosis detection |