The Local Binary Pattern (LBP) Approach for Cataract Disease and Classification Using Convolutional Neural Network (CNN)
Keywords:
Digital Image, Convolutional Neural Network, CataractAbstract
The Eye is one of the human sensory organs that functions as a visual organ. In society, eyes affected by cataracts are often difficult to distinguish from normal eyes, making it challenging for many people to realize when they are showing signs of cataracts. With advancements in technology, the prediction and classification of cataracts have become easier through digital image processing. In this study, the authors conducted predictions using Machine Learning to identify cataract and normal eyes using the Convolutional Neural Network (CNN) method and LBP feature extraction. The dataset used consists of 1,411 digital images with two classes: cataract and normal. The data was split into 80% for training and 20% for testing, and the dataset was obtained from Kaggle, a platform specializing in data science. This study employed 100 epochs and achieved an accuracy of 87% on training data and 85% on test data. The results indicate that the Convolutional Neural Network (CNN) method performs well in predicting eye diseases.
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