scholarly journals The Prototype of Computer-Assisted for Screening and Identifying Severity of Diabetic Retinopathy Automatically from Color Fundus Images for mHealth System in Thailand

2016 ◽  
Vol 86 ◽  
pp. 457-460 ◽  
Author(s):  
Weeagul Pratumgul ◽  
Worawat Sa-ngiamvibool
Keyword(s):  
Diabetic Retinopathy ◽  
Computer Assisted ◽  
Fundus Images ◽  
Color Fundus Images

scholarly journals Developments for Diabetic Retinopathy Detection and Classification: A Review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 134
Author(s):  
Pooja M. Pawar ◽  
Avinash J. Agrawal
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Eye Disease ◽  
Retinal Images ◽  
Screening Methods ◽  
Fundus Images ◽  
Insulin Deficiency ◽  
Vascular Disorder ◽  
Impaired Metabolism ◽  
Color Fundus Images

Diabetes is characterized by impaired metabolism of glucose caused by insulin deficiency. Diabetic retinopathy is the eye disease, is caused by retinal damage which is generally formed as a result of diabetes mellitus. It is a serious vascular disorder for which early detection and the treatment are required to inhibit the intense vision loss. Also, the diagnosis entails skilled professionals for detection because non-automatic screening methods are very time consuming and are not that efficient for a large number of retinal images. This paper provides a broad review of various techniques and methodologies used by the authors for diabetic retinopathy detection and classification. Furthermore, most recent work and developments are studied in this paper. We are proposing an advanced deep learning CNN approach for automatic diagnosis of DR from color fundus images.  

Decision Support System for Diagnosing Diabetic Retinopathy from Color Fundus Images

2020 ◽  
Vol 64 (2) ◽  
pp. 20502-1-20502-10
Author(s):  
Duygu Çelik Ertuğrul ◽  
Yıltan Bitirim ◽  
Basmah Yakoub Anber
Keyword(s):  
Diabetic Retinopathy ◽  
State Of The Art ◽  
Medical Condition ◽  
Measurement Techniques ◽  
Automated System ◽  
Eye Disease ◽  
Data Sets ◽  
Fundus Images ◽  
Diabetic Eye Disease ◽  
Color Fundus Images

Abstract Diabetic Retinopathy (DR) is a medical condition, also known as diabetic eye disease, which is vision-threatening damage to the retina of the eye caused by diabetes. As the technology advances, researchers are becoming more interested in intelligent medical diagnosis systems to assist screening of DR in earlier stages. In this study, variety of state-of-the-art procedures are used to extract the anatomic segments and lesions from the color fundus images. In addition, an automated system is proposed for the detection of anatomic segments and lesions by grading approach to help clinical diagnosis of the DR analysis. Four publicly available databases of color fundus images and various appropriate measurement techniques are used to compare quantitatively the performance of the proposed system. The experiments conducted on DIARETDB0, DIARETDB1, STARE, and HRF data sets have proved that accuracy, sensitivity, and specificity of the proposed system are comparable or superior to state-of-the-art methods.

scholarly journals Validation of computer-aided diagnosis of diabetic retinopathy from retinal photographs of diabetic patients from Telecamps

2021 ◽  
Author(s):  
Dr SHEILA JOHN ◽  
Dr Sangeetha Srinivasan ◽  
Dr Prof Natarajan Sundaram
Keyword(s):  
Diabetic Retinopathy ◽  
Tamil Nadu ◽  
Characteristic Curve ◽  
Posterior Pole ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Fundus Images ◽  
Automated Algorithm ◽  
Screening Performance ◽  
Patients With Diabetes

Objective: To validate an algorithm previously developed by the Healthcare Technology Innovation Centre, IIT Madras, India for screening of diabetic retinopathy (DR),  in fundus images of diabetic patients from telecamps to examine the screening performance for DR. Design: Photographs of patients with diabetes were examined using the automated algorithm for the detection of DR   Setting: Mobile Teleophthalmology camps were conducted in two districts in Tamil Nadu, India from Jan 2015 to May 2017. Participants: 939 eyes of 472 diabetic patients were examined at Mobile Teleophthalmology camps in Thiruvallur and Kanchipuram districts, Tamil Nadu, India,. Fundus images were obtained (40-45-degree posterior pole in each eye) for all patients using a nonmydriatic fundus camera by the fundus photographer. Main Outcome Measures: Fundus images were evaluated for presence or absence of DR using a computer-assisted algorithm, by an ophthalmologist at a tertiary eye care centre (reference standard) and by a fundus photographer. Results: The algorithm demonstrated 85% sensitivity and 80% specificity in detecting DR compared to ophthalmologist. The area under the receiver operating characteristic curve was 0.69 (95%CI=0.65 to 0.73). The algorithm identified 100% of vision-threatening retinopathy just like the ophthalmologist. When compared to the photographer, the algorithm demonstrated 81% sensitivity and 78% specificity. The sensitivity of the photographer to detect DR was found to be 86% and specificity was 99% in detecting DR compared to ophthalmologist. Conclusions: The algorithm can detect the presence or absence of DR in diabetic patients. All findings of vision-threatening retinopathy could be detected with reasonable accuracy and will help to reduce the workload for human graders in remote areas.

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