Automatic Cataract Detection Using the Convolutional Neural Network and Digital Camera Images - meshkin, alireza; Azizi, Farjam; Goudarzi, Khosro (Journal of Ophthalmic and Optometric Sciences, 2023)
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.