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Early detection of diabetic retinopathy (DR) and diabetic macular edema (DME) is difficult due to the presence of abnormalities like micro-aneurysms in the fundus images which causes vision loss for diabetic patients. In this paper, we detect DR and DME in an earlier stage by performing preprocessing to enhance the quality of the input image, which includes three steps: noise filtering, artefact removal and contrast enhancement. Second, blood vessel segmentation is performed, using Improved Mask-Regional Convolutional Neural Networks (Mask RCNN), which increases the accuracy and precision rate of DR and DME detection. Finally, feature extraction and classification using VGG-16 is performed, which extracts structural features, colour and orientation features. Based on the extracted features, the proposed VGG-16 classifies the image into three classes: normal, DR and DME. After detecting DR and DME, we calculate the severity level of the disease using conditional entropy which classifies the severity into mild, moderate and severe. The proposed work is evaluated by two different datasets: IDRiD and MESSIDOR. The performance of the DR and DME detection is evaluated in terms of accuracy (80.7%), sensitivity (93.67%), specificity (93.67%), F1 score (94.61%), ROC curve and AUC curve compared to existing works.

An intelligent approach for detection and grading of diabetic retinopathy and diabetic macular edema using retinal images