Artificial Intelligence for Retina Disease Detection: A Comprehensive Survey

Efficient and accurate detection of retinal diseases is critical for preventing vision loss and improving patient outcomes. This survey provides an in-depth analysis of state-of-the-art deep-learning techniques for diagnosing retinal diseases using fundus images. It examines various Convolutional Neural Network (CNN) architectures, transfer learning strategies, and attention mechanisms optimized for retinal image analysis. The survey also discusses the integration of Generative Adversarial Networks (GANs), multimodal data fusion, and hybrid models combining traditional and deep learning approaches. Additionally, it emphasizes the role of image preprocessing, dataset balancing, and hyperparameter optimization in enhancing model performance.