Analysis of Deep Learning Techniques for Prediction of Eye Diseases: A Systematic Review

The prediction and early diagnosis of eye diseases are critical for effective treatment and prevention of vision loss. The identification of eye diseases has recently been the subject of much advanced research. Vision problems can significantly affect a person's quality of life, limiting their ability to perform daily activities, impacting their independence, and leading to emotional and psychological distress. Lack of timely and accurate identification of the cause of vision problems can lead to significant challenges and consequences. Delayed diagnosis prolongs the period of impaired vision and its associated negative impact on an individual's well-being. Deep learning techniques have emerged as powerful tools for analyzing medical images, including retinal images and predicting various eye diseases. This review provides an analysis of deep learning techniques commonly used for eye disease prediction. The techniques discussed include Convolutional Neural Networks (CNNs), Transfer Learning, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Explainable Deep Learning. The application of these techniques in eye disease prediction is explored, highlighting their strengths and potential contributions. The review emphasizes the importance of collaborative efforts between deep learning researchers and healthcare professionals to ensure the safe and effective integration of these techniques. The analysis highlights the promise of deep learning in advancing the field of eye disease prediction and its potential to improve patient outcomes.