Multiple Retinal Diseases Prediction for Enhancing the Identification of Diabetic Retinopathy

Abstract

The primary causes of vision impairment and blindness are retinal diseases, which include diabetic retinopathy, age-related macular degeneration, glaucoma, and retinal detachment. Correct and timely diagnosis of these illnesses is essential for efficient treatment and patient care. This abstract describes a novel use of convolutional neural networks (CNNs) for the diagnosis and prediction of various retinal diseases. A large dataset of retinal images covering a variety of retinal diseases is gathered and labelled with disease names in this study. To guarantee consistency and improve the model's capacity to pick up pertinent features, these photos go through a thorough preprocessing process. Techniques for data augmentation are used to diversify datasets more. The architecture of a CNN is intended for the categorization of retinal disorders. Convolutional layers are used in this architecture to extract features, and pooling layers are used to reduce dimensionality. Fully connected layers are then used to classify diseases. Using supervised learning methods, the model is trained on the annotated dataset, optimizing the loss function and keeping an eye on validation performance to avoid overfitting. On a different test dataset, the CNN model's performance is evaluated using a number of evaluation metrics, such as accuracy, precision, recall, F1-score, and the AUC-ROC score. Additionally, post-processing steps are used to eliminate predictions with low confidence, increasing the model's clinical usefulness.