Smart phone based automated diabetic retinopathy detection system

Abstract

One of the major causes of blindness across individuals of various age groups and genders is Diabetic Retinopathy (DR). It is mainly caused by Diabetes. The symptoms of DR include seeing an increased number of floaters, blurry vision, vision that changes from blurry to clear sometimes, seeing dark areas in the field of vision, poor night vision and so on. A patient who has been diagnosed with diabetes for a long time is prone to DR. This paper proposes a robust, deep learning based mobile application to address the said issue. The rationale behind the development of the mobile application is to detect DR in far-flung areas, which do not have regular access to healthcare facilities. An input image captured by an ophthalmoscope when fed to the mobile application can give result within a few seconds. The model utilizes convolution neural networks build upon the VGG model. The proposed system quantifies the “confidence level” with a high accuracy of 96 %. Another feature of the proposed system is that it can work offline without any network connectivity, thus making it more useful in remote areas. The application further has a user friendly and intuitive interface.

Keywords

• Diabetic retinopathy
• Convolution neural network
• TensorFlow
• Deep learning
• Flutter