{"id":4459,"date":"2021-11-23T17:07:02","date_gmt":"2021-11-23T22:07:02","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4459"},"modified":"2021-11-23T17:07:04","modified_gmt":"2021-11-23T22:07:04","slug":"senior-capstone-5","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2021\/11\/23\/senior-capstone-5\/","title":{"rendered":"Senior Capstone"},"content":{"rendered":"\n<h3 class=\"has-text-color wp-block-heading\" style=\"color:#2c98ce\">Chest X-Ray Abnormalities Detection with a focus on Infiltration<\/h3>\n\n\n\n<h5 class=\"wp-block-heading\">Abstract<\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Navigating chest X-rays is an obligatory step to determine lung and heart diseases. Since many people now believe that Chest X-ray radiographs can detect COVID-19, the disease of the decade, the problem of Chest X-ray Abnormalities Detection has gained increasing attention from researchers. Numerous machine learning algorithms have been developed to address this problem to raise reading accuracy, improve efficiency, and save time for both doctors and patients. In this work, I propose a model to determine whether a Chest X-ray image has Infiltration and to detect the abnormalities in that image using YOLOv3. The model will be trained and tested with the VinBigData dataset. Overall, I will use the existing tool, YOLOv3, on a new problem of detecting Infiltration in Chest X-ray radiographs.<\/p>\n\n\n\n<p class=\"has-text-color wp-block-paragraph\" style=\"color:#e57773\"><a href=\"https:\/\/drive.google.com\/file\/d\/1GqeFKY1NRhF2Tqb88sq3T8LV4WvQqIap\/view?usp=sharing\">Paper<\/a><\/p>\n\n\n\n<figure class=\"wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\nhttps:\/\/youtu.be\/-ast8Jq3Eeg\n<\/div><\/figure>\n\n\n\n<h5 class=\"wp-block-heading\">Software Architecture Diagram<\/h5>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"956\" height=\"436\" src=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2021\/11\/Software-Architecture-Diagram.png\" alt=\"\" class=\"wp-image-4461\" srcset=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2021\/11\/Software-Architecture-Diagram.png 956w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2021\/11\/Software-Architecture-Diagram-300x137.png 300w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2021\/11\/Software-Architecture-Diagram-768x350.png 768w\" sizes=\"(max-width: 956px) 100vw, 956px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Chest X-Ray Abnormalities Detection with a focus on Infiltration Abstract Navigating chest X-rays is an obligatory step to determine lung and heart diseases. Since many people now believe that Chest X-ray radiographs can detect COVID-19, the disease of the decade, the problem of Chest X-ray Abnormalities Detection has gained increasing attention from researchers. Numerous machine &#8230; <a title=\"Senior Capstone\" class=\"read-more\" href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/2021\/11\/23\/senior-capstone-5\/\" aria-label=\"Read more about Senior Capstone\">Read more<\/a><\/p>\n","protected":false},"author":100,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[],"class_list":["post-4459","post","type-post","status-publish","format-standard","hentry","category-student"],"_links":{"self":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4459","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/comments?post=4459"}],"version-history":[{"count":1,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4459\/revisions"}],"predecessor-version":[{"id":4462,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/posts\/4459\/revisions\/4462"}],"wp:attachment":[{"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/media?parent=4459"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/categories?post=4459"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/portfolios.cs.earlham.edu\/index.php\/wp-json\/wp\/v2\/tags?post=4459"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}