Research on Retinal Screening and Recognition Algorithm Based on Deep Convolutional Network

The rising incidence and diversity of retinal diseases pose a significant threat to visual health. Traditional screening methods, which involve manual analysis of fundus images by professional doctors, are not only time-consuming but also prone to errors. To address this, our study introduces an optimized deep convolutional network architecture tailored for retinal images. This architecture minimizes computing resource consumption while maintaining high precision, thereby enhancing screening efficiency. We propose an algorithm based on a deep convolutional integrated network for screening and recognizing retinal diseases, which automatically learns and extracts features from numerous fundus images to ensure accurate and efficient identification of various retinal conditions. To bolster the model's generalization capabilities, data enhancement techniques were employed, enabling it to maintain robust recognition performance across different image qualities and angles. Additionally, practical considerations such as processing speed and user-friendliness were factored into the design for clinical applications. To further refine recognition accuracy, a model integration strategy was adopted, combining predictions from multiple models to derive the final diagnosis. Experimental results show that our algorithm has high accuracy in screening tasks and high in recognition tasks, proving its high accuracy and strong practical value. This method can serve as an effective diagnostic tool for doctors managing retinal diseases.