Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
<p>Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , , , |
| منشور في: |
2020
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| _version_ | 1864513561284313088 |
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| author | Tawsifur Rahman (14150523) |
| author2 | Amith Khandakar (14151981) Muhammad Abdul Kadir (16869963) Khandaker Rejaul Islam (16869966) Khandakar F. Islam (16869969) Rashid Mazhar (14571265) Tahir Hamid (16869921) Mohammad Tariqul Islam (7854059) Saad Kashem (16869972) Zaid Bin Mahbub (16869975) Mohamed Arselene Ayari (16869978) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author author author author |
| author_facet | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Muhammad Abdul Kadir (16869963) Khandaker Rejaul Islam (16869966) Khandakar F. Islam (16869969) Rashid Mazhar (14571265) Tahir Hamid (16869921) Mohammad Tariqul Islam (7854059) Saad Kashem (16869972) Zaid Bin Mahbub (16869975) Mohamed Arselene Ayari (16869978) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Muhammad Abdul Kadir (16869963) Khandaker Rejaul Islam (16869966) Khandakar F. Islam (16869969) Rashid Mazhar (14571265) Tahir Hamid (16869921) Mohammad Tariqul Islam (7854059) Saad Kashem (16869972) Zaid Bin Mahbub (16869975) Mohamed Arselene Ayari (16869978) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2020-10-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3031384 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Reliable_Tuberculosis_Detection_Using_Chest_X-Ray_With_Deep_Learning_Segmentation_and_Visualization/24015951 |
| 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 Cardiovascular medicine and haematology Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning X-ray imaging Lung Image segmentation Deep learning Diseases Medical diagnostic imaging Tuberculosis detection TB screening Transfer learning Lungs segmentation Image processing |
| dc.title.none.fl_str_mv | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3031384" target="_blank">https://dx.doi.org/10.1109/access.2020.3031384</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1542103ffb9f4dddf45d414bf9aacf28 |
| identifier_str_mv | 10.1109/access.2020.3031384 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24015951 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and VisualizationTawsifur Rahman (14150523)Amith Khandakar (14151981)Muhammad Abdul Kadir (16869963)Khandaker Rejaul Islam (16869966)Khandakar F. Islam (16869969)Rashid Mazhar (14571265)Tahir Hamid (16869921)Mohammad Tariqul Islam (7854059)Saad Kashem (16869972)Zaid Bin Mahbub (16869975)Mohamed Arselene Ayari (16869978)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesMachine learningX-ray imagingLungImage segmentationDeep learningDiseasesMedical diagnostic imagingTuberculosis detectionTB screeningTransfer learningLungs segmentationImage processing<p>Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3031384" target="_blank">https://dx.doi.org/10.1109/access.2020.3031384</a></p>2020-10-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3031384https://figshare.com/articles/journal_contribution/Reliable_Tuberculosis_Detection_Using_Chest_X-Ray_With_Deep_Learning_Segmentation_and_Visualization/24015951CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240159512020-10-15T00:00:00Z |
| spellingShingle | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman (14150523) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning X-ray imaging Lung Image segmentation Deep learning Diseases Medical diagnostic imaging Tuberculosis detection TB screening Transfer learning Lungs segmentation Image processing |
| status_str | publishedVersion |
| title | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| title_full | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| title_fullStr | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| title_full_unstemmed | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| title_short | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| title_sort | Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning X-ray imaging Lung Image segmentation Deep learning Diseases Medical diagnostic imaging Tuberculosis detection TB screening Transfer learning Lungs segmentation Image processing |