Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image

Xu, Kele; Feng, Dawei; Mi, Haibo

doi:10.3390/molecules22122054

Cited by 217 publications

(111 citation statements)

References 17 publications

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“…Implementing AI into DR screening and surveillance services may assist in increasing efficiency in DR management. [58][59][60] This has culminated in a few significant AI MLCs being created that demonstrate classification results similar to trained graders and ophthalmologists. In these studies, MLCs have already proven their capacity to recognize and classify DR from normal images.…”

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

“…In these studies, MLCs have already proven their capacity to recognize and classify DR from normal images. [58][59][60] This has culminated in a few significant AI MLCs being created that demonstrate classification results similar to trained graders and ophthalmologists. 3,38,61 These studies all used large datasets to create the MLCs.…”

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

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Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

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Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age-related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.

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Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

View full text Add to dashboard Cite

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“…The size was resized to 512 x 512 pixels. The results of this research are 91.5% accuracy (Xu, Feng, & Mi, 2017…”

Section: In Research Conducted By Adarsh P Andmentioning

confidence: 65%

Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

Ignatius

Chandra

Bohdan

et al. 2019

j. penelit. pos inform.

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Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.

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“…Furthermore, [23] have developed the model from scratch. They have tested different architecture of the neural network ranges from 9-18 layers, and the convolution kernel size ranges from 1 to 5.…”

Section:  Deep Learning For Detection Of Diabetic Retinopathymentioning

confidence: 99%

A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

Sallam¹,

Olanrewaju²,

Asnawi³

2019

IJITEE

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Nowadays, artificial intelligence applications invade all of the fields including medical applications field. Deep learning, a subfield of artificial intelligence, in particular, Convolutional Neural Networks (CNN), have quickly become the first choice for processing and analyzing medical images due to its performance and effectiveness. Diabetic retinopathy is a vision loss disease that infects people with diabetes. This disease damages the blood vessels in the retina, hence, leads to blindness. Due to the sensitivity and complications involved in managing diabetics, designing and developing automated systems to detect and grade diabetic retinopathy is considered one of the recent research areas in the world of medical image applications. In this paper, the aspects of deep learning field related to diabetic retinopathy have been discussed. Various concepts in deep learning including traditional Artificial Neural Network (ANN) algorithm, ANN drawbacks in context of computer vision and image processing applications, and the best algorithm to overcome ANN drawbacks, CNN, have been elucidated along with the architecture. The paper also reviews an extensive summary of some works in the current research trend and future applications of the DL algorithms in medical image analysis for DR detection and grading. Furthermore, various research gabs related to building such automated systems for medical image analysis have been conferred – such as imbalance dataset which is considered one of the main performance issues that should be handled, the need of high performance computational resources to train deep and efficient models and others. This is quite beneficial for researchers working in the domain of medical image analysis to handle DR.

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Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image

Cited by 217 publications

References 17 publications

Current state and future prospects of artificial intelligence in ophthalmology: a review

Current state and future prospects of artificial intelligence in ophthalmology: a review

Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

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