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A retinal detachment based strabismus detection through FEDCNN.

Ayesha Jabbar1, Muhammad Kashif Jabbar1, Tariq Mahmood2,3

  • 1College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China.

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|October 6, 2024
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Summary

This study introduces a novel approach using Convolutional Neural Networks (CNNs) and eye-tracking data to accurately diagnose ocular strabismus. The developed FedCNN model achieved 95.2% accuracy, improving diagnostic precision for eye conditions.

Keywords:
AmblyopiaConvolutional neural networkFederated learningGaze deviationHealth issuesStrabismus

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Diagnostics

Background:

  • Ocular strabismus is a significant risk factor for amblyopia and vision loss.
  • Current diagnostic methods for strabismus lack sufficient accuracy and reliability, despite advancements in eye-tracking technology.

Purpose of the Study:

  • To enhance the accuracy and reliability of strabismus diagnosis.
  • To develop an automatic strabismus detection system integrating novel algorithms.

Main Methods:

  • A novel FedCNN model was proposed, combining Convolutional Neural Networks (CNNs) with eXtreme Gradient Boosting (XGBoost).
  • The model utilizes Gaze deviation (GaDe) images to capture dynamic eye movements for precise feature extraction.
  • Eye-tracking datasets from subjects were employed for training and validation.

Main Results:

  • The FedCNN model achieved a diagnostic accuracy of 95.2% for strabismus detection.
  • The CNN's detailed connection layer effectively selected features crucial for strabismus recognition.
  • The proposed method demonstrated high precision in diagnosing strabismus.

Conclusions:

  • The developed FedCNN model significantly improves the accuracy of strabismus diagnosis.
  • This approach has the potential to transform eye disease diagnostics for a substantial number of patients.
  • The integration of CNNs and eye-tracking data offers a promising direction for automated ophthalmological diagnostics.