Retinal detachment (RD) is a severe disorder that leads to vision loss, although it can be highly treatable with prompt and appropriate medical intervention. Early detection of RD can increase the probability of successful reattachment and improve visual outcomes, mainly before the macular involvement. Manual screening of RD is tedious and laborious, making it challenging to implement on a wide scale of healthcare applications. Therefore, an automated screening tool for early RD detection is very essential. This work proposes a novel multiclass RD grading framework using a Graph neural network (GNN) model. For this study, the simple linear iterative clustering method has been employed, which considers all samples, training, and testing samples as nodes and establishes a set of edges or connections between them to form a graph structure. In addition, three graph neural networks, namely the graph convolutional network, GraphSAGE, and graph attention network, were used as feature extractors to learn the graph’s semantic relations and local-global features effectively. Finally, an ensemble learning approach with a majority voting mechanism was utilized to assign weights to the retrieved graph features, leading to the final prediction. Speaker(s): Dr. R. Murugan, Virtual: https://events.vtools.ieee.org/m/442009