CNN Technology for Automated Detection of Diabetic Retinopathy

Abstract

Diabetic retinopathy is one of the primary causes of vision loss in people with diabetes globally, or Diabetic Retinopathy. Preventing irreversible visual damage requires early detection and prompt treatment. In recent years, Deep learning methods such as Convolutional Neural Networks (CNNs) have demonstrated promising outcomes in automating the diagnosis and categorization of Diabetic Retinopathy using retinal fundus images. This study presents a thorough examination of CNNs' application in the automated identification of diabetic retinopathy. In order to maximize performance, we investigate several architectures and training approaches, taking into account variables like transfer learning algorithms, augmentation techniques, and dataset size. The efficacy of CNNs in attaining elevated levels of precision, sensitivity, and specificity in drug discovery assignments is evidenced by the outcomes of experiments. Furthermore, we discuss challenges such as dataset biases, interpretability of deep learning models, and deployment in clinical settings. With the potential to improve healthcare outcomes for diabetic patients worldwide, this research adds to the continuing efforts to use AI-driven solutions for the early identification and management of diabetic retinopathy.