Classification of Diabetic Retinopathy Using CNN
Keywords:
Diabetic retinopathy, CNN, Fundus Images, Deep Learning, Transfer learning.Abstract
Diabetic retinopathy affects individuals with diabetes. Diabetes is a prevalent global disease. Currently, about 422 million people have diabetes, putting them at risk of developing diabetic retinopathy, a condition can lead to vision loss or blindness. 25% of diabetics also have diabetic retinopathy, with 5% experiencing complete vision loss. Symptoms include seeing spots or dark floaters, blurry vision, fluctuations in vision, dark or empty patches in vision, and eventual vision loss. The aim of this study is to explore various deep learning techniques using Fundus images for diabetic retinopathy detection. Our project involves utilizing pre-trained CNN models like VGG16 and RESNET50, along with building a CNN model from scratch for multi-level classification of diabetic retinopathy. Features extracted from these models will be used with machine learning classifiers to categorize subjects into different levels of diabetic retinopathy severity. Additionally, transfer learning methods will be employed to address data limitations and enhance training efficiency. The effectiveness of our approach will be evaluated on 80% of the training dataset. We believe that our research can assist ophthalmologists in diagnosing and treating patients more efficiently before the condition worsens.
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