Diabetic Retinopathy Detection Smartphone App Using Tensor Flow
Manoj Kamber , Dr. Priya Swaminarayan
Diabetic retinopathy is a medical term for a disorder that damages the retina and may cause blindness. The illness is most common in people in their working years, and it may lead to severe eyesight loss if left untreated. The continual execution of deep neural organizations on cell phone stages to distinguish and organize diabetic retinopathy from eye fundus images is presented in this study. This execution builds on a previously announced execution by considering each of the five phases of diabetic retinopathy. Binary deep neural organisations are built at principal, one for recognising four phases and the other for further defining the last step into additional two stages, using motion learning and fundus photos from the Eye PACS and APTOS datasets. Then it is shown how these prepared organizations are transformed into a mobile phone program, with Android as well as iOS modifications, to deal with images captured by cell phone cameras on a continual basis. The application is planned so that fundus pictures can be caught and handled progressively by cell phones along with focal point connections that are industrially accessible. The grew constant cell phone application gives a cost effective and generally open methodology for leading first-finish diabetic retinopathy eye tests in quite a while or regions with restricted admittance to fundus cameras and ophthalmologists.
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