{"id":1525,"date":"2022-12-20T12:56:38","date_gmt":"2022-12-20T12:56:38","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1525"},"modified":"2022-12-20T12:56:59","modified_gmt":"2022-12-20T12:56:59","slug":"vol20-no2-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art5\/","title":{"rendered":"Applying the Lifelong Machine Learning Paradigm in Tuberculosis Triage"},"content":{"rendered":"<p>Regina Reis da Costa Alves <a href=\"https:\/\/orcid.org\/0000-0002-6528-3529\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Frederico Caetano Jandre de Assis Tavares <a href=\"https:\/\/orcid.org\/0000-0002-7246-714X\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Anete Trajman <a href=\"https:\/\/orcid.org\/0000-0002-4000-4984\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&#038; Jos\u00e9 Manoel de Seixas <a href=\"https:\/\/orcid.org\/0000-0001-5148-7363\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> 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 recommended 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\u2019 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.<\/p>\n<p><strong>Keywords:<\/strong> Lifelong machine learning, continuous learning, tuberculosis, chest radiographs, pneumonia.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol20-no2-art5\">10.21528\/lnlm-vol20-no2-art5<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art5.pdf\">vol20-no2-art5.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2022\/12\/vol20-no2-art5.bib\">vol20-no2-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regina Reis da Costa Alves , Frederico Caetano Jandre de Assis Tavares , Anete Trajman &#038; Jos\u00e9 Manoel de Seixas Abstract: Tuberculosis (TB) and pneumonia, including pneumonia from SARS-CoV-2 infection, are among the main causes of lower respiratory infections, which <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1512,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1525","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Applying the Lifelong Machine Learning Paradigm in Tuberculosis Triage - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol20-no2\/vol20-no2-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Applying the Lifelong Machine Learning Paradigm in Tuberculosis Triage - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Regina Reis da Costa Alves , Frederico Caetano Jandre de Assis Tavares , Anete Trajman &#038; 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