Shuffle fuzzy attention network for glaucoma detection from fundus images

Key Points

• Detection algorithms significantly improved identification of glaucoma through advanced image processing techniques.
• Glaucoma identification accuracy rose to 94% with fuzzy attention network applied on fundus images—highlighting efficacy in diagnosis.
• Using a shuffle fuzzy attention network with prior model comparisons, the analysis focuses on image processing advancements.
• The work supports enhanced screening for glaucoma, with further validation needed in clinical settings.