Ensemble deep learning for tuberculosis detection
Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges f...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Institute of Advanced Engineering and Science
2020
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/79706/ |
| _version_ | 1848858739946291200 |
|---|---|
| author | Ahmad Hijazi, Mohd Hanafi Yang, Leong Qi Alfred, Rayner Mahdin, Hairulnizam Yaakob, Razali |
| author_facet | Ahmad Hijazi, Mohd Hanafi Yang, Leong Qi Alfred, Rayner Mahdin, Hairulnizam Yaakob, Razali |
| author_sort | Ahmad Hijazi, Mohd Hanafi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively. |
| first_indexed | 2025-11-15T12:18:14Z |
| format | Article |
| id | upm-79706 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T12:18:14Z |
| publishDate | 2020 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-797062022-10-25T02:44:26Z http://psasir.upm.edu.my/id/eprint/79706/ Ensemble deep learning for tuberculosis detection Ahmad Hijazi, Mohd Hanafi Yang, Leong Qi Alfred, Rayner Mahdin, Hairulnizam Yaakob, Razali Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively. Institute of Advanced Engineering and Science 2020 Article PeerReviewed Ahmad Hijazi, Mohd Hanafi and Yang, Leong Qi and Alfred, Rayner and Mahdin, Hairulnizam and Yaakob, Razali (2020) Ensemble deep learning for tuberculosis detection. Indonesian Journal of Electrical Engineering and Computer Science, 17 (2). pp. 1014-1020. ISSN 2502-4752; ESSN: 2502-4760 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20625 10.11591/ijeecs.v17.i2.pp1014-1020 |
| spellingShingle | Ahmad Hijazi, Mohd Hanafi Yang, Leong Qi Alfred, Rayner Mahdin, Hairulnizam Yaakob, Razali Ensemble deep learning for tuberculosis detection |
| title | Ensemble deep learning for tuberculosis detection |
| title_full | Ensemble deep learning for tuberculosis detection |
| title_fullStr | Ensemble deep learning for tuberculosis detection |
| title_full_unstemmed | Ensemble deep learning for tuberculosis detection |
| title_short | Ensemble deep learning for tuberculosis detection |
| title_sort | ensemble deep learning for tuberculosis detection |
| url | http://psasir.upm.edu.my/id/eprint/79706/ http://psasir.upm.edu.my/id/eprint/79706/ http://psasir.upm.edu.my/id/eprint/79706/ |