Enhanced Central Serous Retinopathy Classification Using an Optimized Digital Twin Enabled Domain Adversarial Graph Network for Advanced Analysis of Retinal Images

The human eye visually perceives surrounding objects, and the retina plays a crucial role on capturing light and converting it into electrical signals for further processing by the brain. The eye consists of multiple layers and is divided into anterior and posterior segments. Numerous automated and manual procedures have been developed to identify retinal disorders, but these methods are often uncomfortable for patients and time-consuming. To address these drawbacks, this paper proposes a new technique named Enhanced Central Serous Retinopathy Classification Using an Optimized Digital Twin Enabled Domain Adversarial Graph Network for Advanced Analysis of Retinal Images (CSRC-ODTAGN-RI). Initially, input data is collected form coherence tomography (OCT) image and Fundus image dataset. Then the collected data is given to Adaptive Square Root Cubature Kalman Filter (ASRCKF) to reduce the noise and enhance their quality of the image. Then the preprocessed images are given to the Digital Twin Enabled Domain Adversarial Graph Network (DTAGN) for Central Serous Retinopathy (CSR) classification. The Sea Horse Optimization (SOH) algorithm, inspired by seahorse behavior, is an evolutionary optimization technique used to fine-tune parameters within the DTAGN, enhancing CSR classification. The proposed method attains 26.64%, 15.27%, 30.45% higher accuracy and 26.72%, 10.08%, 30.64% higher f1-score compared to the existing models: Detect the CSR utilizing DL by Retinal Imageries (DCSR-OCT-CNN), Intra and Inter Expert Validation of an Automatic Segmentation Technique for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Imageries (IIEV-FRCSC-OCTI), and MacularNet: Towards Fully Automated Attention-Dependent Deep CNN for Macular Disease Categorization (MNFA-DCNN-MDC) respectively.