Convolution Neural Network Based Approach for Diabetic Retinopathy Detection using Fundus Images
Abstract
Abstract: Diabetic Retinopathy (DR) represents the most prevalent complication arising from diabetes, impacting the retina and standing as a leading cause of global blindness. Timely detection plays a pivotal role in preserving patients' vision, yet early identification remains challenging, relying heavily on clinical experts' interpretation of fundus images. In this investigation, a deep learning model underwent training and validation using a proprietary dataset. The intelligent model assessed the quality of test images, distinguishing them into DR-Positive and DR-Negative categories and further classifying their severity stages, encompassing mild, moderate, severe, and normal. Subsequently, expert review will scrutinize the model's performance based on the obtained results
Metrics
Topics
Related Documents
Convolution neural network based approach for diabetic retinopathy detection using fundus images
Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network
Hasliza Abu HassanMarzuqi YaakobSasni IsmailJuwairiyyah Abd RahmanIzyani Mat RusniAzlee ZabidiAhmad Ihsan Mohd YassinNooritawati Md TahirSuraiya M. Shafie
Automated Diabetic Retinopathy Detection and classification using ImageNet Convolution Neural Network using Fundus Images
C. JayakumariVidhya LavanyaE.P. Sumesh
Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network
Detection of Referable Diabetic Retinopathy From Fundus Image Using Convolution Neural Network
Sandep GuptaAshim KhadkaBidur Devkota