JOINT SPACE NARROWING CLASSIFICATION BASED ON HAND X-RAY IMAGE

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dc.contributor.advisorWu, Minen_US
dc.contributor.authorWang, Fakaien_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-07-10T05:34:09Z
dc.date.available2020-07-10T05:34:09Z
dc.date.issued2020en_US
dc.description.abstractX-ray images have been widely used by radiologists for disease diagnosis. For Rheumatoid Arthritis (RA), Joint Space Narrowing (JSN) is one major symptom that can be read from X-ray images. In this thesis, we investigate the JSN classification for RA diagnosis in terms of methodology, data analysis, neural network models, performance analysis. First, we perform the statistical analysis of X-ray data and design a baseline convolutional neural network (CNN). We show algorithms to extract joint patches. Then we conduct prediction analysis. Second, we design the fusion model to harness the correlation between the same type of joints. Sharing information within one X-ray image would increase the prediction performance. We also compare unified classifiers and separate classifiers. Third, we design the attention map model for joints with complex contexts, which filters out unrelated surroundings. We conclude that our models give good JSN prediction for Rheumatoid Arthritis.en_US
dc.identifierhttps://doi.org/10.13016/1ig9-yc1j
dc.identifier.urihttp://hdl.handle.net/1903/26196
dc.language.isoenen_US
dc.subject.pqcontrolledComputer engineeringen_US
dc.subject.pquncontrolledattention modelen_US
dc.subject.pquncontrolledconvolutional neural networken_US
dc.subject.pquncontrolledfusion modelen_US
dc.subject.pquncontrolledjoint space narrowingen_US
dc.subject.pquncontrolledRheumatoid Arthritisen_US
dc.titleJOINT SPACE NARROWING CLASSIFICATION BASED ON HAND X-RAY IMAGEen_US
dc.typeThesisen_US

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