DeepDR: An image guideddiabetic retinopathy detection technique using attention-based deep learning scheme

This paper proposes an efficient and cost effective deep learning architecture to detect the diabetic retinopathy in real time. Diabetes is a leading root cause of eye disease in patients. It illuminates eye vessels, and releases blood form vessels. Early detection of diabetic retinopathy is useful to reduce the risk of blindness or any hazard. In this paper, after some preprocessing and data augmentation, Inception V3 is used as pre-trained model to extract the initial features set. Convolutional neural network has been used with attention layers. These additional CNN layers are added to extract the deep features to improve classification performance and accuracy. Initially, the model was proposed by Kevin Mader in Kaggle. The paper introduced additional layers in proposed model and improved the validation and testing accuracy significantly. More than 90% validation accuracy was achieved with the proposed Convolutional Neural Network model. Testing accuracy was improved up to 5%. This improvement in accuracy is very significant because the dataset is imbalanced and contains noisy images. It is concluded that global average pooling (GAP) based attention mechanism increased deep learning architecture accuracy to detect the Diabetic Retinopathy in imbalanced and noisy image dataset