Abstract:
This project seeks to address the significant challenge of detecting diabetic retinopathy, a prevalent condition among individuals with diabetes, through non-invasive analysis of retinal images. Traditional detection methods, often reliant on manual examination, are fraught with low accuracy and high error rates, especially in identifying key indicators such as microaneurysms, hemorrhages, and exudates. To overcome these limitations, the initiative introduces an automated diagnostic system that harnesses advanced deep learning technologies, Convolutional Neural Network (CNN or ConvNet) specifically the EfficientNet B3 and ResNet18 models. These models have been trained to analyze retinal images with exceptional accuracy, achieving 98.18% and 99%, respectively, significantly enhancing the precision of diabetic retinopathy diagnosis. The core objective of this research is to revolutionize the diagnosis and monitoring of diabetic retinopathy by providing a reliable, scalable, and userfriendly tool. By automating the detection process, the system not only improves the accuracy of diagnoses but also reduces the strain on healthcare resources, making it an invaluable asset across various healthcare settings, including underserved regions. A standout feature of this solution is its webbased interface, which facilitates easy interaction for healthcare professionals and patients alike, streamlining the process of uploading retinal images and receiving detailed analysis results. Ultimately, this project aims to significantly improve healthcare outcomes for individuals with diabetes by enabling early detection and effective management of diabetic retinopathy. By leveraging cutting-edge technology and medical expertise, the initiative promises to lower the global prevalence of this condition, democratizing access to advanced diagnostic tools, and enhancing the quality of life for affected patients.This innovative approach marks a significant step forward in the fight against diabetic retinopathy, offering hope for better patient care worldwide.
