OPHTHALMIC DISEASE DETECTION USING DEEP LEARNING

Abstract

Abstract—Ophthalmic diseases, such as cataract, glaucoma, and diabetic retinopathy, are significant causes of visual impairment and blindness. Early and accurate detection of these diseases plays a crucial role in ensuring timely interventions and improved patient outcomes. In this paper, we propose a deep learning-based approach for ophthalmic disease detection using the VGG-19 algorithm. A dataset comprising images of various ophthalmic diseases and normal eyes was collected from Kaggle. The dataset was preprocessed, and the VGG-19 model was trained on the labeled images. Performance evaluation was conducted using standard metrics, including accuracy, precision, recall, and F1-score. The results demonstrate the efficacy of the proposed approach in accurately identifying ophthalmic diseases. The VGG-19 model, with its deep architecture and convolutional neural networks, showcases strong performance in image classification tasks. This approach holds promise for assisting healthcare professionals in the early detection and management of ophthalmic diseases. Further improvements and enhancements, such as increasing the dataset size and incorporating additional disease classes, can be explored to refine the model's performance. The proposed methodology has the potential to contribute to the development of automated ophthalmic disease detection systems, thereby facilitating timely interventions and improving patient care. Keywords: Deep Learning,Convolutional Neural Networks(CNN),VGG-19.