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Ensemble deep learning for tuberculosis detection


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.11591/ijeecs.v17.i2.pp1014-1020
Publisher: Institute of Advanced Engineering and Science
Keywords: Tuberculosis detection; Deep learning; Medical image analysis; Ensemble; Image classification
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 25 Oct 2022 02:44
Last Modified: 25 Oct 2022 02:44
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.11591/ijeecs.v17.i2.pp1014-1020
URI: http://psasir.upm.edu.my/id/eprint/79706
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