Research Contributions with Algorithmic Comparison on the Diagnosis of Diabetic Retinopathy

Abstract

The medical field has been revolutionized by the medical imaging system, which plays a key role in providing information on the early life-saving detection of dreadful diseases. Diabetic retinopathy is a chronic visual disease that is the primary reason for the vision loss in most of the patients, who left undiagnosed at the initial stage. As the count of the diabetic retinopathy affected people kept on increasing, there is a necessity to have an automated detection method. The accuracy of the diagnosis of the automatic detection model is related to image acquisition as well as image interpretation. In contrast to this, the analysis of medical images by using computerized models is still a limited task. Thus, different kinds of detection methods are being developed for early detection of diabetic retinopathy. Accordingly, this paper focuses on the various literature analyses on different detection algorithms and techniques for diagnosing diabetic retinopathy. Here, it reviews several research papers and exhibits the significance of each detection method. This review deals with the analysis on the segmentation as well as classification algorithms that are included in each of the researches. Besides, the adopted environment, database collection and the tool for each of the research are portrayed. It provides the details of the performance analysis of the various diabetic detection models and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be accomplished by future researchers in the detection of diabetic retinopathy.