GDPooled transformer: glaucoma detection using pooled attention based transformer with attention mechanism

Key Points

• Enhanced glaucoma detection is achieved with the novel pooled attention mechanism, improving diagnostic accuracy.
• The approach leverages a transformer architecture that utilizes attention mechanisms for precise identification.
• This method outperforms conventional techniques by integrating machine learning strategies for optimal results.
• The implications of this method highlight the need for further validation in clinical settings to ensure reliability.