Paper Contents
Abstract
The objective of this project is to develop a retinal image analysis system using deep learning techniques for the detection of diabetes-based eye diseases. Diabetic retinopathy and diabetic macular edema are significant complications of diabetes that can lead to vision loss if not detected and treated early. Traditional methods of analysis rely on manual examination by ophthalmologists, which can be time-consuming and subjective. Therefore, there is a need for an automated and accurate approach to assist in the early detection and diagnosis of these conditions. In this project, we propose a deep learning-based system that leverages convolutional neural networks (CNNs) for retinal image analysis. A large dataset of labeled retinal images will be collected and preprocessed to enhance the quality of the data. The deep learning model will be trained using these images, where it will learn to extract relevant features associated with diabetes-based eye diseases. The trained model will then be validated using a separate set of retinal images to assess its performance and generalization ability. The results of the deep learning model will be analyzed, focusing on its accuracy, precision, and ability to classify retinal images into different disease categories. The system will provide diagnostic reports based on the analysis, including information about the presence or absence of specific eye diseases, severity levels, and recommendations for further medical assessment or treatment. By automating the analysis of retinal images, this system aims to improve the efficiency and accuracy of diagnosing diabetes-based eye diseases. It has the potential to assist healthcare professionals in making timely and informed decisions, leading to early interventions and improved patient outcomes. The utilization of deep learning techniques in retinal image analysis has the potential to revolutionize the field and contribute to the overall management of diabetes-related eye diseases.
Copyright
Copyright © 2023 Ananthu Prasad. This is an open access article distributed under the Creative Commons Attribution License.
