The Role of Artificial Intelligence in Enhancing Diabetic Retinopathy Lesion Detection: A Review
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Date
2024-12
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Sri Devaraj Urs Academy of Higher Education
Abstract
Diabetic retinopathy represents a significant microvascular complication associated with prolonged diabetes mellitus and serves as a leading cause of blindness, particularly in developing nations. For the patient's vision to be adequately preserved, early identification of DR is essential. In order to treat the disease, the patient must maintain his or her current level of vision since the disease is irreversible. The Clinical diagnosis demands significant time and the specialized knowledge of an experienced ophthalmologist and also identifying the disease features in images is also more challenging, particularly in the early stages of the disease when disease features are less noticeable. Therefore, deep learning algorithms have been used for the early diagnosis of DR in recent years, and medical image analysis utilising machine learning has demonstrated to be effective in evaluating retinal fundus images. This review's objective is to go over the numerous Deep learning techniques for automated computer-aided analysis of microaneurysms, haemorrhages, and exudates were also addressed, along with a knowledge gap in DR identification. As part of future research, this review seeks to systematize the available algorithms for ease of use and guidance by researchers.
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Keywords
Diabetic Retinopathy Review, Microaneurysms, Haemorrhages, Exudates, Red Lesions, Deep Learning
Citation
Lakshmi KS, Sargunam B.. The Role of Artificial Intelligence in Enhancing Diabetic Retinopathy Lesion Detection: A Review. Journal of Clinical and Biomedical Sciences. 2024 Dec; 14(4): 121-128