Paper Contents
Abstract
Retinal diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration, are significant causes of visual impairment and blindness worldwide. Early detection of these diseases is crucial for effective treatment and prevention of vision loss. In recent years, deep learning-based methods have shown promising results in disease detection from medical images. This study proposes a novel approach for detecting retinal diseases using convolutional neural networks (CNN) on retina images.Our approach involves the use of a CNN architecture to analyze retina images and classify them as normal or abnormal. The CNN is trained on a large dataset of retina images labeled with corresponding disease diagnoses. The network consists of multiple convolutional and pooling layers, followed by fully connected layers for classification. We also employ data augmentation techniques to increase the size and diversity of the training dataset. Our system is able to detect diseases with high sensitivity and specificity, indicating its potential for real-world applications.
Copyright
Copyright © 2025 Vaishnavi Dilip Borade, Diksha Ashok Jagtap, Pranjal Ramprasad Avhad, Saloni Nandkishor Hiray, Ms. S. A. Bhavsar. This is an open access article distributed under the Creative Commons Attribution License.
