A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease

Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data in medical image analysis. In the first phase, the model learns useful visual features from a large collection of unlabeled retinal images using self-supervised training. In the second phase, it is fine-tuned on a smaller set of labeled images for the classification of RP and STGD. Experimental results demonstrate that using 5844 unlabeled and 782 labeled fundus images showed that our method, based on the EfficientNet-B1 architecture, outperforms state-of-the-art supervised learning methods, achieving 98.15% accuracy and 99.68% AUC. The proposed method is flexible and scalable, making it well-suited for real-world applications where labeled data is scarce.