Classifying Glaucoma Using Machine Learning Techniques
Santos, D.
Show abstract
Glaucoma is a common eye disease that can lead to blindness if not detected and treated early. In this paper, we present a machine learning-based approach for classifying glaucoma. We use a publicly available dataset of retinal images and extract features using convolutional neural networks. We compare the performance of different classifiers, including random forest, support vector machine, and XGBoost, and evaluate their accuracy, precision, recall, and F1 score. Our results show that the XGBoost classifier achieves the highest accuracy and F1 score, indicating its potential for diagnosing glaucoma in clinical practice.
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