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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/496
DC FieldValueLanguage
dc.contributor.authorVo Nguyen Nhat Anhen_US
dc.date.accessioned2020-03-12T17:02:13Z-
dc.date.available2020-03-12T17:02:13Z-
dc.date.issued2018-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/496-
dc.description.abstractLung disease is a serious problem nowadays. The number of patients with lung disease is getting higher every year. Unfortunately, not everyone has a chance to be treated with advanced technology. This is because of the cost, and experts, doctors shortage. The current state of computer-aided diagnosis systems is also not effective and efficient enough to support doctors in reducing workload. Hence, it is necessary and urgent to develop such system that can support the work of doctors and as the result help more patients to get in contact with those innovations. This paper aims to build such system dueling specifically with lung disease. The system can learn to segment the lung region in the X-ray image by applying deep learning - a fast-growing technology in recent years. With the help of this diagnosis system, the work required to investigate further lung-related problems will probably decrease. Although there is still a lot of work ahead to complete the system, the proposed approach has shown promising results and points out that deep learning is going to assist medical tremendously.-
dc.language.isoen-
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectComputer-aided diagnosis system-
dc.subjectLung segmentation-
dc.subjectDeep learning-
dc.subjectConvolutional neural network-
dc.subjectLung disease-
dc.subjectDiagnosis system-
dc.titleLung segmentation in chest X-ray image using deep neutral network-
dc.typeThesis-
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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