Authors: Moch Deny Pratama, Royal Fajar Sultoni, Adil Sandy Wardhani, Maulana Hassan Sechuti, Yerezqy Bagus, Dina Zatusiva Haq, Yoga Ari Tofan
Year: 2025
Publisher: IJCONSIST: International Journal Of Computer, Network Security and Information System
Type: Journal
DOI / URL: https://doi.org/10.33005/ijconsist.v7i1.160
Abstract
This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.
Keywords: Classification of Eye Diseases, Convolutional Neural Network, Alexnet, Deep Learning
Citation
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Links
- Publisher Page: https://ijconsist.org/index.php/ijconsist/article/view/160
- Google Scholar: https://scholar.google.com/scholar?cluster=9341093393877158497