Artificial Intelligence-Powered Retinal Fundus Image Evaluation and Synthesis for Ocular Disease Diagnosis and Prevention

This paper presents a method for generating synthetic fundus images using Neural Style Transfer and Cycle-Consistent Generative Adversarial Network, aimed at enhancing the visualization of retinal diseases, such as diabetic retinopathy. The Cycle-Consistent Generative Adversarial Network approach achieved an overall accuracy of 98.3%, with 100.0% precision, 96.0% recall, and an F1-score of 98.0%. Additionally, a comparison evaluated by health specialists yielded an accuracy of 95.8% for real images. By creating realistic synthetic images, the study seeks to raise awareness about ocular health risks and promote self-care among patients. The effectiveness of the synthetic images is validated against real images through advanced machine learning models, which classify retinopathy stages and identify biomarkers. Evaluations by ocular health specialists further confirm the similarity and utility of the synthetic images for diagnostic standardization and improving AI models in healthcare. The results indicate significant potential for these synthetic approaches to enhance early detection and treatment outcomes in the field of ophthalmology. Limitations and challenges faced were considered and detailed as implications for future work.