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Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent. [ABSTRACT FROM AUTHOR]

In recent years, much attention has been provided to developing the systems used for making medical diagnosis. That is because there are various developments made in the field of computing technology. The knowledge related to health and type of disease is essential for making a medical diagnosis by reliable and having an accurate detection. Regarding diabetic retinopathy (DR), it is a common cause for blindness. This disease can be detected in an early stage. It has various symptoms. The most distinctive symptoms are Micro-aneurysms (MAs), Hemorrhages (HAs) which are dark lesions and Hard Exudates (HEs), and Cotton Wool Spots (CWS) that are deemed as bright lesions. Through the present research, the researchers aimed at proposing an automated system for detecting exudates and cotton wool spots in an early stage and classifying them. Regarding the location of optic disc and the structure of blood vessels, they play a significant role in bright lesions for having the DR detected in an early stage. This paper presents algorithms and techniques for processing the retinal image, blood vessel segmentation and extraction, optic disc localization and removal, feature extraction and finally classification for different bright lesions using SVM and Naïve-Bayes classifiers. For testing retinal images, the researchers used image-Ret database which is a publicly available database. This database includes two sub-databases (i.e., DIARETDB0, and DIARETDB1). Finally, we compare the performance of recently published works and our proposed work. [ABSTRACT FROM AUTHOR]