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Amnia Salma
Department of Mathematics, Mathematics and Natural Science Faculty, Universitas Indonesia, Depok, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology

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Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images Amnia Salma; Alhadi Bustamam; Devvi Sarwinda
Nusantara Science and Technology Proceedings Bioinformatics and Biodiversity Conferences (BBC)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.0701

Abstract

The number of people who have Diabetes is about 422 million in the world. Diabetes is a group of metabolic disease characterized by elevated lev- els of blood glucose. The serious damage of blood vessels caused by Diabetes in the tissue at the retina is called Diabetic Retinopathy. Diabetic Retinopathy can cause severe blindness. Early detection can help patients find a suitable treatment and prevent the blindness. Opthalmologists can detect this disease by screening, but this method takes a long time, is very costly and need pro- fessional skills to perform it. In the big data era, many researchers use deep learning models for medical help. One of the models use image classification. We have designed a tool using image classification to help ophthalmologists detect diabetic retinopathy. In this research, we use image classification to classify Diabetic Retinopathy into two classes which are normal (No DR) and Diabetic Retinopathy. We use 200 datasets of fundus images that we obtain from Kaggle Database. We used deep learning model in this research that is one of Convolutional Neural Network architecture called GoogleNet. For training the model we used Python as programming languange with Pytorch library. GoogleNet has a very good performance for image classification and has an accuracy of 88%.
Co-Authors Bustamam, Alhadi Devvi Sarwinda