A Comprehensive Study of Ocular Disease Prediction Through Multi-Modal Fusion Using Deep Learning Approach

In recent years, the prevalence of ocular diseases has surged globally, necessitating advanced diagnostic methods to enhance early detection and treatment outcomes. This study presents a comprehensive analysis of ocular disease prediction through using deep learning techniques, aiming to address the limitations of traditional diagnostic methods by leveraging the strengths of multiple data sources. A key aspect of the study is the application of advanced deep learning models, specifically convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for temporal and sequential data processing. In conclusion, this research provides valuable insights into the application of multi-modal fusion in ocular disease prediction and sets the stage for future developments in integrating diverse data types for more accurate and timely diagnosis.