Developing an intelligent model to detect the level of diabetic retinopathy using blood vessel pattern extraction in retinal images

Diabetic retinopathy is a chronic and progressive eye disease in which the human retina is affected by an increase in the amount of insulin in the blood. Diabetic retinopathy, if not detected and treated in time, threatens the patient's vision and eventually causes complete blindness. Among various clinical symptoms, microaneurysm appears as the first sign of diabetic retinopathy. Accurate and reliable detection of microaneurysms is a challenging problem due to its small size and low contrast. The successful detection of microaneurysms will be more useful for the proper treatment of the disease in its early stages. In this paper, we present a method for classifying medical images of the retina to accurately detect the level of development of diabetic retinopathy. Our proposed method has six main steps. In steps one to four, the input image is pre-processed. In the first step; the detection and segmentation of blood vessels using the morphological closing operation is done. The second step; performs circular edge detection using gradient morphological operation. The third step; optical disc detection using the circular Hough transform edge detection method is done. The fourth step; the detection and segmentation of microaneurysms is done by removing blood vessels, circular edges, and optical discs and we use circular Hough transformation. In the fifth step, feature extraction is performed by considering two features, blood vessel area and microaneurysm area, and four features obtained from the gray level co-occurrence matrix. Finally, the sixth step is classification using the SVM classifier (Gaussian kernel function). We evaluated the performance of the model using EyePacs retinal fundus image database and obtained 95.20% and 97% accuracy and specificity, respectively. Experimental results show that our proposed model performs better in terms of evaluated measures compared to other methods.