Paper Contents
Abstract
Diabetic Retinopathy (DR) is a progressive disease and a leading cause of blindness among adults. Early diagnosis and treatment of DR can prevent vision loss and improve outcomes. However, manual examination by a trained specialist is currently the standard for DR diagnosis, which is time-consuming and limited by the availability of specialists in remote areas. In this study, we propose a deep learning approach for DR diagnosis using Convolutional Neural Networks (CNNs). The study was conducted on a large dataset of retinal fundus images, which were pre-processed and labeled according to the severity of DR. A CNN was trained using this dataset to perform binary and multi-class classification of DR. The performance of the proposed model was evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The results showed that the proposed CNN model achieved high accuracy in DR diagnosis, outperforming traditional machine learning methods. The model was able to learn complex features from the retinal fundus images and make accurate predictions. The study highlights the potential of deep learning in DR diagnosis and the importance of developing automated methods for early detection and treatment of the disease. The findings of this study have implications for the widespread implementation of automated DR screening in clinical practice, especially in resource-limited settings, where access to specialized care is limited. The proposed model can be used as a decision-support tool for healthcare providers, helping to improve the accuracy and efficiency of DR diagnosis.
Copyright
Copyright © 2023 LAKSHMANAN N. This is an open access article distributed under the Creative Commons Attribution License.
