5 Key Takeaways

• 1

  A hybrid deep learning framework combines Chaotic Adaptive Poplar-Bacteria Optimization and Cataract VisionNet for improved cataract diagnosis.

• 2

  The approach achieved 99.10% accuracy, 99% precision, and 99.21% recall on the Eye Cataract Kaggle dataset.

• 3

  Cha-PO optimizes feature selection to reduce dimensionality and enhance computational efficiency in cataract detection.

• 4

  CVNet integrates lightweight convolutional layers and transfer learning to improve diagnostic performance while maintaining efficiency.

• 5

  The system shows potential for broader applications in cataract severity grading and monitoring, pending external validation.