{"id":1777,"date":"2020-12-05T10:30:38","date_gmt":"2020-12-05T03:30:38","guid":{"rendered":"http:\/\/research.binus.ac.id\/airdc\/?p=1777"},"modified":"2021-09-01T12:36:17","modified_gmt":"2021-09-01T05:36:17","slug":"a-fast-and-accurate-model-of-thoracic-disease-detection-by-integrating-attention-mechanism-to-a-lightweight-convolutional-neural-network","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2020\/12\/a-fast-and-accurate-model-of-thoracic-disease-detection-by-integrating-attention-mechanism-to-a-lightweight-convolutional-neural-network\/","title":{"rendered":"A Fast and Accurate Model of Thoracic Disease Detection by Integrating Attention Mechanism to a Lightweight Convolutional Neural Network"},"content":{"rendered":"

The utilization of Deep Learning, especially the Convolutional Neural Network (CNN), is currently the best approach for thoracic disease detection from Chest X-Ray images. However, CNN typically has a slow runtime, hence potentially can introduce a bottleneck in the healthcare that uses the technology. To answer the challenge, we proposed a model that integrates an attention mechanism to a lightweight CNN. The proposed model can run faster than the current state-of-the-art model for thoracic disease detection while having the second-best performance among the thoracic disease detection models.<\/p>\n

International Conference on Computer Science and Computational Intelligence 2020<\/div>\n
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Branden Adam Sangerokia and Tjeng Wawan Cenggoro<\/strong><\/div>\n
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The utilization of Deep Learning, especially the Convolutional Neural Network (CNN), is currently the best approach for thoracic disease detection from Chest X-Ray images. However, CNN typically has a slow runtime, hence potentially can introduce a bottleneck in the healthcare that uses the technology. To answer the challenge, we proposed a model that integrates an […]<\/p>\n","protected":false},"author":14,"featured_media":2015,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1777","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1777","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=1777"}],"version-history":[{"count":4,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1777\/revisions"}],"predecessor-version":[{"id":1908,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1777\/revisions\/1908"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/2015"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=1777"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=1777"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=1777"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}