Abstract
Diabetic retinopathy (DR) is a retinal condition caused by diabetes that remains one of the leading causes of avoidable blindness globally. Early identification and proper treatment may help avoid vision loss. Deep learning (DL), by outsourcing the analysis of retinal pictures, has the potential to greatly enhance DR screening and diagnosis. This paper looks at the most current breakthroughs in deep learning algorithms used to identify and classify diabetic retinopathy. It focuses on a range of approaches, including deep learning, support vector deeps, and convolutional neural networks. We evaluate their effectiveness by comparing the accuracy, sensitivity, and specificity measurements published in recent research. The paper also discusses the integration of deep learning models into clinical processes, problems such as data scarcity and model interpretability, and future research prospects. Healthcare practitioners may improve patient outcomes in diabetic eye care by using deep learning to get more accurate and early diagnosis. Retinal fundus image datasets are commonly sourced from publicly available medical image repositories which provide labeled images for training and evaluating diagnostic models. To analyze this data, both traditional and advanced machine learning techniques are employed.
KEYWORDS
Diabetic Retinopathy, Early Detection, Deep Learning, Neural Networks, Image Processing, MATLAB Vision Threat, Retinal Images, Denoising, Classification.
Arvind Joshi1, Dhramandra Sharma2*, Sandeep Kumar Tiwari3, Anand Kumar Singh4
1,2,3,4Department of Computer Science and Engineering, Vikrant University, Gwalior-474005, M.P., India