Diabetic Retinopathy: Present and Past

Gupta, Ankita; Chhikara, Rita

doi:10.1016/j.procs.2018.05.074

Cited by 53 publications

(25 citation statements)

References 29 publications

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“…After the optical disk is recognized it is eliminated using a mask. The mask is added on top of the optical disk to block it from further detection (2,5,6) .…”

Section: Optical Disk Detection and Eliminationmentioning

confidence: 99%

“…From the above-segmented image, the accuracy obtained is 78%. In review article (6) , different techniques for the classification of the various features like haemorrhages, blood vessels, and microaneurysms are reviewed and compared. The paper gives a comparison of the tentative results of various parameters like sensitivity, accuracy, specificity, and area under curve accuracy and concludes that algorithm of deep neural network obtains best result.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of different stages in Diabetic retinopathy from retinal fundus images using radial basis function based SVM

Behera¹

2020

IJST

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“…After the optical disk is recognized it is eliminated using a mask. The mask is added on top of the optical disk to block it from further detection (2,5,6) .…”

Section: Optical Disk Detection and Eliminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of different stages in Diabetic retinopathy from retinal fundus images using radial basis function based SVM

Behera¹

2020

IJST

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“…Patients having DR for much more reason are prone to blindness. Advanced DR and macular edema (clinically significant) and also lead to vision loss [16].…”

Section: Daibetic Retinopathymentioning

confidence: 99%

The CURRENT TRENDS AND TREATMENTS IN DIABETIC RETINOPATHY

Kanna

2019

Asian J Pharm Clin Res

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The drastically increasing issues of the disease scenario currently are with different types of diabetes all over the world. It has been reported, approximately 592 million are suffering from the disease throughout the world. It affects differently in different patients with the disease. There have been reports that it is affected differently and also has different side effects. It is also been reported that diabetes mellitus leads to the cause of diabetic retinopathy (DR) and also diabetic macular edema. It is considered as one of the most common causes which is linked to DR. DR has been considered as one of the most important causes for the loss of vision or impaired vision. The drugs show different types of incompatibility such as toxicity, solubility issues, aggregation, and chemical degradation these can be improved by applying several methods. DR is classified according to “Airlie House” into different categories and based on different strategies and consideration. It was found that DR is the main cause for vision loss and also there no much strategies for development of new treatment. The treatment involved is laser photocoagulation and vitrectomy, among these the effective treatment, was found to be laser photocoagulation. This is mainly characterized as proliferative and non-proliferative DR. Different therapeutic agents have been taken for the study these includes vascular endothelial growth factor, renin-angiotensin system inhibitors and nonsteroidal anti-inflammatory drugs, they are certainly different interventions for the treatment, they are nanotechnology and liposome. Nanotechnology applied is the most effective and also acceptable way of treatment.

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“…Diabetic retinopathy (DR) is one of the most common microvascular complications that is caused by DM, and it happens when the blood vessels inside the retina are affected by high blood levels [5]. DR can create some irreversible complications that can lead to blindness in many cases.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020

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Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

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Diabetic Retinopathy: Present and Past

Cited by 53 publications

References 29 publications

Prediction of different stages in Diabetic retinopathy from retinal fundus images using radial basis function based SVM

Prediction of different stages in Diabetic retinopathy from retinal fundus images using radial basis function based SVM

The CURRENT TRENDS AND TREATMENTS IN DIABETIC RETINOPATHY

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

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