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dc.contributor.advisorArif, Hossain
dc.contributor.authorMaliha, Maisha
dc.contributor.authorTareque, Ahmed
dc.contributor.authorRoy, Sourav Saha
dc.date.accessioned2018-05-15T04:20:53Z
dc.date.available2018-05-15T04:20:53Z
dc.date.copyright2018
dc.date.issued2018-04-04
dc.identifier.otherID 14101013
dc.identifier.otherID 17341009
dc.identifier.otherID 13301087
dc.identifier.urihttp://hdl.handle.net/10361/10149
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (page 30-32).
dc.description.abstractDiabetic Retinopathy (DR) is human eye disease among people with diabetics which causes damage to retina of eye and may eventually lead to complete blindness. Detection of diabetic retinopathy in early stage is essential to avoid complete blindness. Effective treatments for DR are available though it requires early diagnosis and the continuous monitoring of diabetic patients. Also many physical tests like visual acuity test, pupil dilation, and optical coherence tomography can be used to detect diabetic retinopathy but are time consuming. The objective of our thesis is to give decision about the presence of diabetic retinopathy by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image. It will give us accuracy of which algorithm will be suitable and more accurate for prediction of the disease. Decision making for predicting the presence of diabetic retinopathy is performed using K-Nearest Neighbor, Random Forest, Support Vector Machine and Neural Networks.en_US
dc.description.statementofresponsibilityMaisha Maliha
dc.description.statementofresponsibilityAhmed Tareque
dc.description.statementofresponsibilitySourav Saha Roy
dc.format.extent32 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectDiabetic retinopathyen_US
dc.subjectEye diseaseen_US
dc.subjectPupil dilationen_US
dc.subjectTomographyen_US
dc.subjectMachine learningen_US
dc.titleDiabetic retinopathy detection using machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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