Retinal Image Classification by using Continual Learning

Retinal image classification is vital for the early diagnosis and management of various ocular diseases, including diabetic retinopathy, age-related macular degeneration, and glaucoma. Traditional machine learning approaches face significant challenges in adapting to evolving datasets, often requiring periodic retraining, which can lead to the loss of previously learned knowledge due to a phenomenon known as catastrophic forgetting. To address this issue, we propose a novel approach utilizing continual learning techniques. Our framework allows for the continuous incorporation of new retinal image data while preserving previously acquired knowledge, thereby mitigating the effects of catastrophic forgetting. By integrating advanced continual learning algorithms, our method ensures that the model can adapt to new data without sacrificing the accuracy of previous classifications. We validate our approach through extensive experiments on benchmark retinal image datasets, demonstrating that it achieves superior classification accuracy compared to conventional methods. This enhanced performance highlights the effectiveness of continual learning in maintaining high accuracy and adaptability in retinal image classification systems, offering a promising solution for the dynamic nature of retinal data and significantly improving diagnostic capabilities and patient outcomes.