Diabetic Retinopathy Classification through Convolutional Neural Network

M.Nandhini, R.Harini

Diabetic Retinopathy; Image Classification; Convolutional Neural Network; Resnet 18

An innovative approach to diabetic retinopathy classification utilizing convolutional neural networks (CNNs) is presented in this research. Diabetic retinopathy, a severe complication of diabetes and a leading cause of blindness worldwide, necessitates accurate and timely diagnosis for effective treatment and management. A deep learning framework based on ResNet-18 architecture is developed to automatically classify retinal images into different stages of diabetic retinopathy. The proposed model demonstrates noteworthy accuracy and efficiency. Experimental results on a large dataset validate the effectiveness of the approach, offering potential applications in clinical practice for early detection and intervention of diabetic retinopathy. The findings of this research offer significant implications for enhancing healthcare outcomes in ophthalmology through the integration of artificial intelligence.

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