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dc.contributor.advisorJayashree Kalpathy-Cramer.en_US
dc.contributor.authorShahrawat, Malika.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-11-23T17:39:22Z
dc.date.available2020-11-23T17:39:22Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/128573
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June, 2019en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 44-48).en_US
dc.description.abstractThe retina has biomarkers not only for ophthalmic disease but also for diseases and conditions across the entire body. In this paper, I focus on retinopathy of prematurity (ROP), a proliferative vascular disease that can cause blindness in prematurely born infants. To prevent further disease progression and vision loss in infants with ROP, early and accurate detection of plus disease is crucial. In current practice, clinicians compare retinal fundus photographs to a reference standard image in order to detect plus disease for severe ROP. This process can be highly qualitative, subjective, and variable. Furthermore, some clinical environments may lack clinicians with the expertise to diagnose these diseases. I am to address these shortcomings in current clinical diagnosis of ROP by using deep learning methods to automatically extract biomarkers of disease without human intervention. Since ROP is primarily present in prematurely born infants, I also attempt to predict and analyze gestational and postmenstrual age, and how disease predictions vary from healthy to affected infants.en_US
dc.description.statementofresponsibilityby Malika Shahrawat.en_US
dc.format.extent48 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleUnderstanding the biomarkers of retinal disease using deep learningen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1220877293en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-11-23T17:39:20Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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