Diabetic Retinopathy Detection From Fundus Images Using Multi-Tasking Model With EfficientNet B5

Abstract

Diabetic Retinopathy (DR) is a common eye disease that affects over 3 million people annually. People with diabetes are more prone to suffer from Diabetic Retinopathy. This condition can cause blurring of vision and blindness. Early detection and treatment are the most effective ways to manage Diabetic Retinopathy. Due to the huge number of diabetic patients and the need for more accurate and automatic diagnosis, the development of deep neural networks has been acknowledged. One of the issues with deep learning classification tasks, particularly in the medical field, is a lack of labelled training data. Transfer learning enables the deep learning model to be trained with a minimal Training Dataset. In Deep convolutional networks, transfer learning can be utilized to solve the problem of insufficient training data. Previous studies on deep neural networks have been promising. Through a color fundus photography study, we have developed a model to identify the different stages of Diabetic Retinopathy using Deep learning. Our proposed model has achieved 87% of accuracy with the help of EfficientNet model. The main aim of this work is to develop a robust system for detecting DR automatically.

Keywords

• cnn
• focal loss
• resnet
• efficientnet