Deep Learning Based Diabetic Retinopathy Detection from Fundus Images with Mobile Application & Web Solution

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dc.contributor.advisorK. M. A. Salam
dc.contributor.authorZillur Rahman
dc.contributor.authorNusrat Jahan Khan Shila
dc.contributor.id1510154642
dc.contributor.id1512879642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-01
dc.date.accessioned2025-07-01T11:30:10Z
dc.date.available2025-07-01T11:30:10Z
dc.date.issued2020
dc.description.abstractDiabetic retinopathy is one of the leading causes of blindness today, and the number of patients is growing at an unprecedented rate as the world’s population increases. At the time of the first diagnosis of diabetes, up to 21% of people with type 2 diabetes have been screened for diabetic retinopathy. As the number of patients grows exponentially, it poses two challenges for the future. The challenges are: access to traditional screening tests will be limited if any modern innovative technology does not bridge the demand-supply gap, and if the traditional system attempts to accommodate a larger number of patients with fewer resources, there is a possibility of error and failing to comply with health guidelines and standards. A deep learning-based, scalable software solution may be a game changer in dealing with those unprecedented challenges. As deep learning achieves state-of-the-art performance in many fields, even outperforming experts, it will ultimately restrict inaccurate and faulty diagnoses, and a robust software solution will democratize access to this health technology. This paper proposes a deep neural network or deep learning-powered automated system for screening and grading diabetic retinopathy with 88% accuracy using 299x299 pixel fundus images using a modular android smartphone application.
dc.description.degreeUndergraduate
dc.identifier.cd600000395
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1222
dc.language.isoen
dc.publisherNorth South University
dc.rights©Nsulibrary
dc.titleDeep Learning Based Diabetic Retinopathy Detection from Fundus Images with Mobile Application & Web Solution
dc.typeProject
oaire.citation.endPage75
oaire.citation.startPage1
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