Automatic Diabetic Retinopathy Detection Using Resnet50 and Inceptionv3

Abstract

Diabetic retinopathy (DR) is an eye disease in diabetic patients and is the cause of blindness in the population. Because of the complexity of color fundus images DR classification by humans is challenging and is also an error prone task. Thus, this paper proposes automated DR classification to detect diabetic retinopathy in advance. Traditional machine learning algorithms like Logistic regression (60%) and Support Vector Machines (70%) have been employed to detect the severity of DR using information from retina images in this work. Since machine learning Algorithms provide low accuracy, deep learning algorithms have been used to improve the accuracy of the model. To develop a model from scratch is a complicated task, which can be solved using another technique called transfer learning. In this work, two CNN models are trained - Resnet50 and Inception V3 with pre-trained imageNet weights. ResNet50 is used in two ways, as a classifier, fine tuning and InceptionV3 without fine tuning. The CNN model achieved a good accuracy around (85%) using InceptionV3 without fine tuning. The dataset used was provided by Kaggle which are high resolution images of the retina which are proven to show good accuracy. Since machine learning Algorithms provide low accuracy, deep learning algorithms have been used to improve the accuracy of the model. To develop a model from scratch is a complicated task, which can be solved using another technique called transfer learning. In this work, two CNN models are trained - Resnet50 and Inception V3 with pre-trained imageNet weights. ResNet50 is used in two ways, as a classifier, fine tuning and InceptionV3 without fine tuning. The CNN model achieved a good accuracy around (85%) using InceptionV3 without fine tuning. The dataset used was provided by Kaggle which are high resolution images of the retina which are proven to show good accuracy.