2020 IEEE Conference on Big Data and Analytics (ICBDA) 2020
DOI: 10.1109/icbda50157.2020.9289822
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Diabetic Retinopathy Grading Using ResNet Convolutional Neural Network

Abstract: Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classif… Show more

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Cited by 11 publications
(2 citation statements)
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“…Recently, ResNet, a deep CNN, was proposed to address the problem brought about by imbalanced datasets in DR grading. 27 Additionally, a bagging ensemble of three CNNs: a shallow CNN, VGG16, and InceptionV3, was used to classify images as DR, glaucoma, myopia and normal. 28 Previously, a transformer was also proposed by Vaswani et al 29 for natural language processing tasks especially for machine translation.…”
Section: Related Workmentioning
confidence: 99%
“…Recently, ResNet, a deep CNN, was proposed to address the problem brought about by imbalanced datasets in DR grading. 27 Additionally, a bagging ensemble of three CNNs: a shallow CNN, VGG16, and InceptionV3, was used to classify images as DR, glaucoma, myopia and normal. 28 Previously, a transformer was also proposed by Vaswani et al 29 for natural language processing tasks especially for machine translation.…”
Section: Related Workmentioning
confidence: 99%
“…The pre-trained model is also known as the source model, while the new dataset and task is referred to as the target data and target task. A common example is to take a computer vision model, trained to identify everyday objects in millions of images, and further train this model for grading diabetic retinopathy on only a few thousand fundus photographs, instead of training the model from scratch on this smaller dataset [ 5 ]. This example demonstrates a type of transfer learning known as fine-tuning, or weight or parameter transfer.…”
Section: Introductionmentioning
confidence: 99%