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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

Computer Systems Science & Engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar P.Saranya Vijayakumar Ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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