Automatic Cataract Detection Using the Convolutional Neural Network and Digital Camera Images
Journal of Ophthalmic and Optometric Sciences,
Vol. 5 No. 3 (2021),
15 March 2023
https://doi.org/10.22037/joos.v5i3.40379
Abstract
Background: The cataract is the most prevalent cause of blindness worldwide and is responsible for more than 51 % of blindness cases. As the treatment process is becoming smart and the burden of ophthalmologists is reducing, many existing systems have adopted machine-learning-based cataract classification methods with manual extraction of data features. However, the manual extraction of retinal features is generally time-consuming and exhausting and requires skilled ophthalmologists.
Material and Methods: Convolutional neural network (CNN) is a highly common automatic feature extraction model which, compared to machine learning approaches, requires much larger datasets to avoid overfitting issues. This article designs a deep convolutional network for automatic cataract recognition in healthy eyes. The algorithm consists of four convolution layers and a fully connected layer for hierarchical feature learning and training.
Results: The proposed approach was tested on collected images and indicated an 90.88 % accuracy on testing data. The keras model provides a function that evaluates the model, which is equal to the value of 84.14 %, the model can be further developed and improved to be applied for the automatic recognition and treatment of ocular diseases.
Conclusion: This study presented a deep learning algorithm for the automatic recognition of healthy eyes from cataractous ones. The results suggest that the proposed scheme outperforms other conventional methods and can be regarded as a reference for other retinal disorders.
- Cataract
- Deep Learning
- Convolutional Neural Network
- Image Processing
How to Cite
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