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Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer

Juan J Gómez-Valverde1,2, Alfonso Antón3,4,5, Gianluca Fatti3

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This study demonstrates that Convolutional Neural Networks (CNNs) can effectively detect glaucoma from fundus images. A VGG19 transfer learning model achieved expert-level performance, offering a valuable tool for large-scale screening.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma detection from color fundus images is complex and requires significant expertise.
  • Current methods often rely on manual interpretation, which can be time-consuming and prone to variability.

Purpose of the Study:

  • To evaluate the performance of various Convolutional Neural Network (CNN) schemes for glaucoma detection in fundus images.
  • To investigate the impact of dataset size, CNN architecture, transfer learning, and clinical history integration on detection accuracy.
  • To compare CNN-based system performance against human expert evaluators.

Main Methods:

  • Exploration of different CNN architectures, including newly defined and transfer learning approaches (e.g., VGG19).
  • Utilized three distinct datasets comprising a total of 2313 fundus images.
  • Integrated patient clinical history data alongside fundus images for analysis.
  • Performance evaluation metrics included Area Under the Curve (AUC), sensitivity, and specificity.

Main Results:

  • The VGG19 transfer learning scheme achieved the highest performance with an AUC of 0.94.
  • Sensitivity and specificity ratios were comparable to those of expert human evaluators.
  • Dataset size and architecture significantly influenced CNN performance.

Conclusions:

  • CNNs, particularly with transfer learning using VGG19, show significant promise for automated glaucoma detection.
  • The developed system demonstrates performance on par with human experts, suggesting its utility for large-scale screening.
  • Integration of clinical history data may further enhance diagnostic capabilities.