Abstract: An innovative approach to enhance the early detection of multiple eye diseases, including glaucoma, cataract, diabetes-related eye conditions, and various infections. The significance of early diagnosis in preventing irreversible vision impairment cannot be overstated, and emerging technologies in the fields of machine learning (ML) offer unprecedented opportunities to revolutionize ophthalmic healthcare. The proposed system employs a comprehensive dataset comprising diverse instances of eye diseases to train a sophisticated ML and DL model. Leveraging state-of-the-art algorithms, including Recurrent neural networks (RNNs) and convolutional neural networks (CNNs), two of the model's methods, are designed to evaluate a variety of ocular properties extracted from medical images. These features include structural abnormalities, textural patterns, and contextual information, enabling the system to discriminate between healthy and diseased conditions with a high degree of accuracy. To validate the effectiveness of the developed model, extensive experimentation will be conducted using a diverse set of real-world eye images sourced from clinical databases. The project aims not only to achieve high accuracy in disease identification but also to optimize the model for real-time applications, ensuring its practical utility in clinical settings.
Keywords: Deep Learning, Convolutional neural networks, Machine learning, Eye diseases, Medical images
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DOI:
10.17148/IJARCCE.2025.14263
