A retinal detachment based strabismus detection through FEDCNN

Abstract

Abstract Ocular strabismus, a common condition in the present generation is an absolute risk factor for amblyopia and blinding premorbid visual loss. Despite the availability of new optometry tools with eye-tracking data, the issues persist in attaining accuracy and reliability in diagnosing strabismus. These two concerns are specifically accommodated in this study by the proposed novel approach that involves CNNs with eye-tracking datasets from subjects. The presented work aims to improve the accuracy of diagnostics in ophthalmology utilizing the integration of the further proposed algorithms into an automatic strabismus detection system. For this purpose, the proposed FedCNN model combines the CNN with eXtreme Gradient Boosting (XGBoost) and uses the Gaze deviation (GaDe) images to capture dynamic eye movements. This method tries to make the feature extraction as accurate as possible in its best working state to enhance the diagnosis precision. The model proves to be accurate, reaching 95.2%, which is even more prominent because of the more or less detailed connection layer of the CNN, which is used for the selection of features designated for such tasks of strabismus recognition. The presented method has the potential of shifting the approach to diagnosing diseases of the eyes in more or less half of the patients.

Keywords

• Federated learning
• Convolutional neural network
• Strabismus
• Gaze deviation
• Amblyopia
• Health issues