APPLYING THE LIFELONG MACHINE LEARNING PARADIGM IN TUBERCULOSIS TRIAGE
APPLYING THE LIFELONG MACHINE LEARNING PARADIGM IN TUBERCULOSIS TRIAGE
| dc.contributor.author | Alves, Regina Reis da Costa | |
| dc.contributor.author | Tavares, Frederico Caetano Jandre de Assis | |
| dc.contributor.author | Trajman, Anete | |
| dc.contributor.author | Seixas, Jose Manoel de | |
| dc.date.accessioned | 2025-01-24T15:12:54Z | |
| dc.date.available | 2025-01-24T15:12:54Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Tuberculosis (TB) and pneumonia, including pneumonia from SARS-CoV-2 infection, are among the main causes of lower respiratory infections, which are the fourth cause of death worldwide. Recently, the World Health Organization rec- ommended the use of computer-aided diagnosis (CAD) software as a tool to analyze chest radiographs (CXR) for TB screening and triage. Most CAD developed to date aim to screen exclusively for TB. This work applies the lifelong machine learning paradigm to detect both pneumonia and TB through CXRs and evaluate the models’ ability to retain and acquire knowledge. Two well-known lifelong learning models, the Efficient Lifelong Learning Algorithm (ELLA) and Learning without Forgetting (LwF), were applied to two public CXR datasets containing TB and pneumonia samples together with healthy CXR samples. Pneumonia detection was learned first and TB detection was learned as second task. The SP index, a function of sensitivity and specificity, was used to evaluate the models. We concluded that both algorithms were able to retain knowledge about pneumonia detection and were also able to learn TB detection. | |
| dc.identifier.citation | Alves RRC, Tavares FCJA, Trajman A, Seixas JM. APPLYING THE LIFELONG MACHINE LEARNING PARADIGM IN [Artigo publicado no Learning and Nonlinear Models - Journal of the Brazilian Society on Computational Intelligence (SBIC), Vol. 20, Iss. 2, pp. 63-73, 2022] Sociedade Brasileira de Inteligência Computacional. doi: 10.21528/lnlm-vol20-no2-art5. | |
| dc.identifier.other | DOI: 10.21528/lnlm-vol20-no2-art5 | |
| dc.identifier.uri | https://dspace.inc.saude.gov.br/handle/123456789/736 | |
| dc.language.iso | en | |
| dc.publisher | Sociedade Brasileira de Inteligência Computacional | |
| dc.subject | Lifelong machine learning | en |
| dc.subject | continuous learning | en |
| dc.subject | tuberculosis | en |
| dc.subject | chest radiographs | en |
| dc.subject | pneumonia. | en |
| dc.title | APPLYING THE LIFELONG MACHINE LEARNING PARADIGM IN TUBERCULOSIS TRIAGE | |
| dc.type | Article |