Recent advancements in deep learning techniques are revolutionizing the prediction and diagnosis of various eye diseases through the analysis of retinal images.

Studies have shown significant improvements in diagnosing conditions such as diabetic retinopathy and glaucoma, with advanced AI models achieving high precision, recall, and F1-scores.

Several research efforts have utilized datasets from Kaggle, employing different architectures like EfficientNet and VGG models to classify ocular diseases with promising results.

Eye diseases, including cataracts, glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy, can lead to severe vision impairment and blindness if not diagnosed early.

Uncorrected myopia is a major global cause of distance vision impairment, with severe forms increasing the risk of serious complications.

AMD is a leading cause of vision loss in individuals over 50, influenced by both genetic and environmental factors.

The ongoing research aims to enhance early detection and treatment of eye diseases, ultimately improving patient outcomes through timely interventions.

Traditional diagnostic methods for eye diseases are often labor-intensive and prone to errors, highlighting the necessity for automated techniques.

AI and machine learning algorithms present cost-effective solutions for the early detection of eye diseases, particularly beneficial in underdeveloped countries.

The proposed CNN model demonstrated impressive performance metrics, achieving a macro accuracy of 97% and individual class accuracies exceeding 95%, indicating its potential for clinical application.

The conclusion underscores the importance of AI in enhancing the accuracy and timeliness of eye disease diagnoses, emphasizing the need for ongoing advancements in this field.

Various AI techniques, including convolutional neural networks (CNNs) and transfer learning, have been effectively applied to diagnose multiple eye diseases, showcasing high accuracy and efficiency.