Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
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 u...
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IEEE
2020-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9224622/ |
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| author | Tawsifur Rahman Amith Khandakar Muhammad Abdul Kadir Khandaker Rejaul Islam Khandakar F. Islam Rashid Mazhar Tahir Hamid Mohammad Tariqul Islam Saad Kashem Zaid Bin Mahbub Mohamed Arselene Ayari Muhammad E. H. Chowdhury |
| author_facet | Tawsifur Rahman Amith Khandakar Muhammad Abdul Kadir Khandaker Rejaul Islam Khandakar F. Islam Rashid Mazhar Tahir Hamid Mohammad Tariqul Islam Saad Kashem Zaid Bin Mahbub Mohamed Arselene Ayari Muhammad E. H. Chowdhury |
| author_sort | Tawsifur Rahman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d2a9254331be4948af19234b0c77f376 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d2a9254331be4948af19234b0c77f3762025-08-25T23:00:40ZengIEEEIEEE Access2169-35362020-01-01819158619160110.1109/ACCESS.2020.30313849224622Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and VisualizationTawsifur Rahman0https://orcid.org/0000-0002-6938-6496Amith Khandakar1https://orcid.org/0000-0001-7068-9112Muhammad Abdul Kadir2https://orcid.org/0000-0002-4535-9215Khandaker Rejaul Islam3https://orcid.org/0000-0003-2863-8156Khandakar F. Islam4Rashid Mazhar5https://orcid.org/0000-0003-4255-8996Tahir Hamid6Mohammad Tariqul Islam7https://orcid.org/0000-0002-4929-3209Saad Kashem8Zaid Bin Mahbub9Mohamed Arselene Ayari10https://orcid.org/0000-0002-8663-886XMuhammad E. H. Chowdhury11https://orcid.org/0000-0003-0744-8206Department of Biomedical Physics and Technology, University of Dhaka, Dhaka, BangladeshDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Biomedical Physics and Technology, University of Dhaka, Dhaka, BangladeshDepartment of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka, BangladeshDepartment of Electrical Engineering, Qatar University, Doha, QatarThoracic Surgery, Hamad General Hospital, Doha, QatarDepartment of Medicine, Weill Cornell Medicine-Qatar, Doha, QatarDepartment of Electrical, Electronic, and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, MalaysiaFaculty of Robotics and Advanced Computing, Qatar Armed Forces-Academic Bridge Program, Qatar Foundation, Doha, QatarDepartment of Mathematics and Physics, North South University, Dhaka, BangladeshCollege of Engineering, Qatar University, Doha, QatarDepartment of Biomedical Physics and Technology, University of Dhaka, Dhaka, BangladeshTuberculosis (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.https://ieeexplore.ieee.org/document/9224622/Tuberculosis detectionTB screeningdeep learningtransfer learninglungs segmentationimage processing |
| spellingShingle | Tawsifur Rahman Amith Khandakar Muhammad Abdul Kadir Khandaker Rejaul Islam Khandakar F. Islam Rashid Mazhar Tahir Hamid Mohammad Tariqul Islam Saad Kashem Zaid Bin Mahbub Mohamed Arselene Ayari Muhammad E. H. Chowdhury Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization IEEE Access Tuberculosis detection TB screening deep learning transfer learning lungs segmentation image processing |
| 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 | Tuberculosis detection TB screening deep learning transfer learning lungs segmentation image processing |
| url | https://ieeexplore.ieee.org/document/9224622/ |
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