Cataract Disease Detection Using Pre-trained Models | ||||
| International Integrated Intelligent Systems | ||||
| Volume 1, Issue 2, June 2024 PDF (467.4 K) | ||||
| DOI: 10.21608/iiis.2024.357771 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Merna Youssef1; Kareem Hassan1; Mohanad Deif2; Rania Elgohary2 | ||||
| 1Department of Biomedical Engineering Faculty of Engineering Cairo University | ||||
| 2Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt | ||||
| Abstract | ||||
| Early detection and prevention of Cataract disease can effectively contribute in reducing the impact of cataracts. In this study, we explore the effectiveness of deep learning algorithms implemented with three pre-trained models —MobileNet VGG19, and ResNet50— for cataract disease detection. These algorithms leverage image processing techniques and have shown promise in various computer vision tasks. Our objective is to predict which algorithm performs best in cataract detection. We use a dataset of retinal fundus images to train and evaluate the models. The results demonstrate the potential of deep learning in early cataract diagnosis, which can significantly improve patient outcomes. Our model was able to achieve an accuracy of 96.33%. | ||||
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