Automatic Detection and Classification of     Diabetic Retinopathy Stages in Retinal Image

Labib, Md. Abrar; Irtiza, Saquib

dc.contributor.author	Labib, Md. Abrar
dc.contributor.author	Irtiza, Saquib
dc.date.accessioned	2020-10-19T17:36:43Z
dc.date.available	2020-10-19T17:36:43Z
dc.date.issued	2018-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/549
dc.description	Supervised by Mr. A.B.M Ashikur Rahman Lecturer	en_US
dc.description.abstract	This paper summarizes the different imaging techniques and methodologies used to perform classification of the different stages of a disease called diabetic retinopathy. In particular, it focuses on deep learning techniques to perform such detection in the fundus images of the patient’s eye. Diabetic retinopathy is a progressive disease that causes the patient to lose eyesight if not diagnosed and treated at an early stage. Ophthalmologists usually diagnose the patient of this disease by screening the retinal fundus images to look for lesions. But the inaccuracy of such diagnosis together with the delay between diagnosis and treatment motivated researchers to automate this process of diagnosis. Using neural networks to train the system on a set of training images, it is possible to make systems that are more accurate and faster than human experts.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Automatic Detection and Classification of Diabetic Retinopathy Stages in Retinal Image	en_US
dc.type	Thesis	en_US