Convolutional neural networks in diabetic eye disease detection: A survey on retinopathy and macular edema

Preethi Kulkarni () and Srinivasa Reddy. K ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 1, 1207-1218

Abstract: Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are notable eye disorders affecting the retina’s inner side. This retina helps a person see and distinguish between colors, which is crucial for performing daily activities. It has been observed in long-term diabetic patients, and the count gradually increases. Manually identifying its presence can be time-consuming and may not yield accurate results, as it can lead to various stages that, if delayed, can cause visual Impairment. Emerging technologies and advancements in medical care have enabled automated mechanisms to perform this task. Machine learning (ML) and Deep learning (DL) are two emerging fields of Artificial Intelligence that help identify grading severity through retinal fundus images at early stages and properly treat patients. The paper reviews convolutional neural networks (CNN) with hybrid models of ML and DL algorithms to implement and achieve it, along with the assets, limitations, and gaps of each mechanism, and helps improve further research.

Keywords: Deep learning; CNN; Diabetic macular edema; Diabetic retinopathy; Eye disease; Machine learning. (search for similar items in EconPapers)
Date: 2025
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