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...
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| Main Authors: | , , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-02-01.
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| Online Access: | Get fulltext |
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| LEADER | 02363 am a22003373u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20625_13360 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Ahmad Hijazi, Mohd Hanafi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Qi Yang, Leong |e author |
| 700 | 1 | 0 | |a Alfred, Rayner |e author |
| 700 | 1 | 0 | |a Mahdin, Hairulnizam |e author |
| 700 | 1 | 0 | |a Yaakob, Razali |e author |
| 245 | 0 | 0 | |a Ensemble deep learning for tuberculosis detection |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20625 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Tuberculosis detection, Deep learning, Medical image analysis Ensemble, Image classification | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 17, No 2: February 2020; 1014-1020 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v17.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20625/13360 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20625/13360 |z Get fulltext |