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dc.contributor.advisorAli, Md. Haider
dc.contributor.advisorAli, Mohammad Hammad
dc.contributor.authorZaman, Asif Uz
dc.contributor.authorBashir, Shadaab Kawnain
dc.date.accessioned2016-05-29T16:39:52Z
dc.date.available2016-05-29T16:39:52Z
dc.date.copyright2016
dc.date.issued2016-04-20
dc.identifier.otherID 12301018
dc.identifier.otherID 13301092
dc.identifier.urihttp://hdl.handle.net/10361/5413
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 37-38).
dc.description.abstractDiabetic retinopathy is a leading problem throughout the world and many people are losing their vision because of this disease. The disease can get severe if it is not treated properly at its early stages. The damage in the retinal blood vessel eventually blocks the light that passes through the optical nerves which makes the patient with Diabetic Retinopathy blind. Therefore, in our research we wanted to find out a way to overcome this problem and thus using the help of convolutional neural network (ConvNet), we wereable to detect multiple stages of severity for Diabetic Retinopathy.There are other processes present to detect Diabetic Retinopathy and one such process is manual screening, but this requires a skilled ophthalmologist and takes up a huge amount of time. Thus our automatic diabetic retinopathy detection technique can be used to replace such manual processes and theophthalmologist can spend more time taking proper care of the patient or at least decrease the severity of this disease.en_US
dc.description.statementofresponsibilityAsif Uz Zaman
dc.description.statementofresponsibilityShadaab Kawnain Bashir
dc.format.extent38 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectComputer science and engineeringen_US
dc.subjectDiabetic retinopathyen_US
dc.titleDiabetic retinopathy detection using image-processingen_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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