Classification of Diabetic Retinopathy and Retinal Vein Occlusion in Human Eye Fundus Images by Transfer Learning

2020 ◽  
pp. 642-653
Author(s):  
Ali Usman ◽  
Aslam Muhammad ◽  
A. M. Martinez-Enriquez ◽  
Adrees Muhammad
Keyword(s):  
Diabetic Retinopathy ◽  
Retinal Vein Occlusion ◽  
Transfer Learning ◽  
Fundus Images ◽  
Human Eye ◽  
Vein Occlusion

scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

Treatment Patterns and Clinical Outcomes for Branch Retinal Vein Occlusion: An 8-Year Experience at a Tertiary Eye Center

2021 ◽  
pp. 247412642097887
Author(s):  
Terry Lee ◽  
Cason B. Robbins ◽  
Akshay S. Thomas ◽  
Sharon Fekrat
Keyword(s):  
Visual Acuity ◽  
Diabetic Retinopathy ◽  
Retinal Vein Occlusion ◽  
Real World ◽  
Early Treatment ◽  
Branch Retinal Vein Occlusion ◽  
Treatment Patterns ◽  
Vein Occlusion ◽  
Anti Vegf ◽  
Treatment Naïve

Purpose: This work aims to investigate real-world treatment patterns and outcomes in eyes with branch retinal vein occlusion in the antivascular endothelial growth factor (anti-VEGF) era. Methods: A retrospective, nonrandomized, comparative study was conducted on eyes diagnosed with branch retinal vein occlusion at a single tertiary center between 2009 and 2017. Medical history, treatment patterns, and visual acuity outcomes were examined. Subanalysis was performed for eyes that met the eligibility criteria for the BRAVO (Ranibizumab for the Treatment of Macular Edema Following Branch Retinal Vein Occlusion) trial. Results: A total of 315 eyes were included, of which 244 were treatment naive. In all eyes, the most common first treatment was the following: intravitreal bevacizumab (38.4%), aflibercept (15.1%), ranibizumab (8.1%), sectoral scatter laser (6.2%), and triamcinolone (3.1%). At 1 year, treatment-naive eyes had received an average of 2.43 anti-VEGF injections. During follow-up, treatment-naive eyes gained an average of 0.21 Early Treatment Diabetic Retinopathy Study lines. Forty eyes that met BRAVO trial criteria received an average of 5.05 anti-VEGF injections in the first year and gained an average of 1.83 Early Treatment Diabetic Retinopathy Study lines. Conclusions: This real-world cohort received fewer anti-VEGF injections at year 1 and experienced less improvement in visual acuity during the course of treatment than clinical trial participants. Trial-eligible patients received more injections and had greater visual gains than those who would not have been eligible for the trial.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

scholarly journals Detection of Multi-Class Retinal Diseases Using Artificial Intelligence: An Expeditious Learning Using Deep CNN with Minimal Data

2019 ◽  
Vol 12 (3) ◽  
pp. 1577-1586 ◽  
Author(s):  
Karthikeyan S. ◽  
Sanjay Kumar P. ◽  
R J Madhusudan Madhusudan ◽  
S K Sundaramoorthy Sundaramoorthy ◽  
P K Krishnan Namboori3
Keyword(s):  
Real Time ◽  
Retinal Vein Occlusion ◽  
Central Retinal Vein Occlusion ◽  
Research Work ◽  
Diagnostic Strategy ◽  
Fundus Images ◽  
Vein Occlusion ◽  
Deep Cnn ◽  
The Right ◽  
Central Retinal Vein

The health-related complications such as diabetes, macular degeneration, inflammatory conditions, ageing and fungal infections may cause damages to the retina and the macula of the eye, leading to permanent vision loss. The major diseases associated with retina are Arteriosclerotic retinopathy (AR), Central retinal vein occlusion (CRVO), Branch retinal artery occlusion (BRAO), Coat's disease (CD) and Hemi-Central Retinal Vein Occlusion (HRVO). The symptomatic variations among these disorders are relatively confusing so that a systematic diagnostic strategy is difficult to set in. Therefore, an early detection device is required that is capable of differentiating the various ophthalmic complications and thereby helping in providing the right treatment to the patient at the right time. In this research work, 'Deep Convolution Neural Networks (Deep CNN) based machine learning approach has been used for the detection of the twelve major retinal complications from the minimal set of fundus images. The model was further cross-validated with real-time fundus images. The model is found to be superior in its efficiency, specificity and ability to minimize the misclassification. The “multi-class retinal disease” model on further cross-validation with real-time fundus image of the gave an accuracy of 95.63 %, validation accuracy of 92.99 % and F1 score of 91.96 %. The multi-class model is found to be a theranostic clinical support system for the ophthalmologist for diagnosing different kinds of retinal problems, especially BRAO, BRVO, CRAO, CD, DR, HRVO, HP, HR, and CN.

Sign in / Sign up

Export Citation Format

Share Document