Self-supervised learning model leveraging structural similarity index for glaucoma recognition

Glaucoma is a leading cause of irreversible blindness worldwide, affecting millions annually. Early diagnosis is crucial but challenging due to the asymptomatic nature of the disease and the complexity of clinical signs. Advanced ocular imaging generates complex, voluminous data, demanding significant expertise for interpretation. Artificial intelligence (AI) is transforming this field by enabling efficient data analysis. We present a comprehensive approach that combines advanced image preprocessing techniques, leveraging the Discrete Fourier Transform (DFT) implemented via the Fast Fourier Transform (FFT) algorithm, with state-of-the-art AI models to enhance diagnostic accuracy. Using the REFUGE2 dataset, this pre-processing achieved high visual fidelity, with SSIM values of 0.9225 for training and 0.8247 for testing. For classification, we employed Transformer architectures, including Masked Image Modeling (MIM) and Masked Autoencoder (MAE), integrated with Self-Supervised Learning (SimCLR). This multi-model approach yielded an accuracy of 90.06% with MIM and 90.14% with MAE on preprocessed images, surpassing the performance achieved on non-preprocessed data. The MIM model demonstrated a strong discriminative capability, highlighted by an AUC-ROC of 0.9298. Our findings show that combining robust preprocessing with Transformer networks significantly improves efficiency and diagnostic accuracy in the early detection of glaucoma.