DEEP-LEARNING BASED FUNDUS ANALYSIS FOR EARLY DETECTION AND MANAGEMENT OF DIABETIC RETINOPATHY

Diabetic retinopathy (DR) is a progressive condition that can cause vision loss. It starts out subtly and gets worse with time.  It affects approximately 35% of people with diabetes worldwide.  According to research, a new case of diabetic retinopathy is diagnosed every few minutes. In its early stages, retinal images are frequently difficult to recognize due to their complexity. In the area of medical imaging, Deep Learning is growing.  Convolutional Neural Networks (CNN) and other architectures are used in this study to see how they can be used to accurately detect and classify the stages of diabetic retinopathy. Our approach utilizes publicly available datasets and several deep learning techniques are used to identify and categories the Fundus images into four stages of DR will be compared in this work.  Model robustness is enhanced using data preprocessing methods like normalization, augmentation, and segmentation. The models are evaluated using performance metrics like accuracy, precision, recall, F1-score, and Area under Curve (AUC).  The results demonstrate that deep learning models can achieve high classification accuracy, outperforming traditional machine learning methods.  Ophthalmologists may find it easier to comprehend model predictions if they are able to gain insight into the regions of interest that are essential for decision-making through visual interpretation of the models. This study underscores the potential of deep learning to revolutionize diabetic retinopathy diagnosis, offering a foundation for future research in integrating multi-modal data and real-world applications.