Paper Key : IRJ************883
Author: Kalyani Palle
Date Published: 01 Feb 2025
Abstract
Diabetic retinopathy is a serious eye disease that can lead to blindness if not diagnosed early. Current methods for diagnosing the disease rely on expert analysis of retinal images, which can be time-consuming and detailed. To overcome this challenge, we propose an improved approach for diabetic retinopathy detection using convolutional neural networks (CNNs) that integrate spatial listening mechanisms. In this project, we developed a deep learning model that uses CNN to analyze retinal images and automatically identify signs of diabetic retinopathy. Our project differs from existing diabetic retinopathy research by integrating the tracking process into the CNN architecture. The spatial attention mechanism helps the model identify important points in the image, improving its ability to detect signs of disease. The aim of the program is to support the development of effective and efficient diagnostic tests and improve patient outcomes through timely diagnosis and treatment.
DOI Requested