Intelligent and Integrated Framework for Exudate Detection in Retinal Fundus Images

Diabetic Retinopathy (DR) is a disease of the retina caused by diabetes. The existence of exudates in the retina is the primary visible sign of DR. Early exudate detection can prevent patients from the severe conditions of DR An intelligent framework is proposed that serves two purposes. First, it highlights the features of exudate from fundus images using an image processing approach. Afterwards, the enhanced features are used as input to train Alexnet for the detection of exudates. The proposed framework is comprised on three stages that include pre-processing, image segmentation, and classification. During the preprocessing stage, image quality is enhanced using contrast enhancement, median filtering, and removal of Optic Disc (OD). In the segmentation stage, image segmentation is performed using morphological operations. During the classification phase, these segmented images are then applied as input to Alexnet for fine-tuning of features and classification. A new data set of fundus images is also developed using data from a local hospital. CNN classifier Alexnet is applied on a newly developed local data set of fundus retinal images with 96.6% accuracy and on a benchmark public research data set DIARETDB1 with 98.88% accuracy. The statistical evaluation and comparative analysis of the proposed approach with existing state-of-the-art techniques validate that the proposed framework is robust and outperforms other methods for early detection of exudates in the fundus.

Automated summary

The study proposes an intelligent framework to highlight exudate features in fundus images and detect exudates using Alexnet. By developing a new dataset of fundus images from a local hospital, CNN classifier Alexnet achieved high accuracy on both the newly developed local dataset and a benchmark public research dataset.