Diabetic Retinopathy Detection: An Automatic Modelling Using Artificial Intelligence & Deep Learning

Diabetic retinopathy (DR) is a leading cause of vision loss among individuals with diabetes. Early detection and timely treatment of DR are crucial to prevent irreversible damage. In recent years, artificial intelligence (AI) and deep learning techniques have shown promising results in automating the detection of DR from retinal images. This paper presents an automatic modeling approach for diabetic retinopathy detection, leveraging the power of AI and deep learning. The proposed method utilizes a convolutional neural network (CNN) architecture designed to analyze retinal images and classify them into different stages of diabetic retinopathy. To evaluate the performance of the proposed model, extensive experiments are conducted on a publicly available diabetic retinopathy dataset. The model's accuracy, sensitivity, specificity, and other performance metrics are assessed using standard evaluation protocols. The results demonstrate that the proposed automatic modeling approach achieves highly accurate and reliable diabetic retinopathy detection. The model shows excellent performance in distinguishing different stages of DR, enabling early intervention and appropriate treatment strategies. The implications of this research are significant, as the automatic detection of diabetic retinopathy using AI and deep learning techniques can aid healthcare professionals in the early diagnosis and management of the disease.