A COMPARATIVE ANALYSIS OF ASSORTED DEEP AND MACHINE LEARNING TECHNIQUES FOR AUTOMATED EARLY DIAGNOSIS OF DIABETIC RETINOPATHY

Emmy Bhatti, Prabhpreet Kaur

Abstract


Diabetic Retinopathy is the most common form of diabetic eye disease. Diabetic Retinopathy (DR) usually only affects people who have had diabetes (diagnosed or undiagnosed) for a significant number of years. In spite of DR not being a curable illness, it can be treated if it is detected in its early stages. The main objective of this paper is to review the various deep learning, machine learning approaches in correspondence with image processing for diagnosing Diabetic Retinopathy as early as possible. Firstly, we have a look at how automated detection systems are constructed and highlight various features used for the purpose of DR diagnosis. Next we analyze the existing work in the field focusing both techniques individually. The papers have been compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained. Finally, we present an overall comparison between deep learning and machine learning algorithms used to detect diabetic retinopathy and conclude why deep learning approaches provide best results when it comes to early detection of the disease.

Keywords


Diabetic retinopathy, Automated diagnosis, Deep learning, Machine learning, Lesion detection.

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DOI: https://doi.org/10.26483/ijarcs.v9i1.5368

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