Computer-assisted identification of proliferative diabetic retinopathy in color retinal images

2015 ◽  
Author(s):  
Garima Gupta ◽  
S. Kulasekaran ◽  
Keerthi Ram ◽  
Niranjan Joshi ◽  
Mohanasankar Sivaprakasam ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Retinal Images ◽  
Computer Assisted

scholarly journals Enhanced CAE system for detection of exudates and diagnosis of diabetic retinopathy stages in fundus retinal images using soft computing techniques

2019 ◽  
Vol 25 (2) ◽  
pp. 131-139
Author(s):  
Sathya D Janaki ◽  
K Geetha
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Retinal Image ◽  
Visual Analysis ◽  
Support Vector ◽  
Retinal Images ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Soft Computing Techniques ◽  
Cae System

Abstract Diabetic Retinopathy (DR) is one of the leading causes of visual impairment. Diabetic Retinopathy is the most recent technique of identifying the intensity of acid secretion in the eye for diabetic patients. The identification of DR is performed by visual analysis of retinal images for exudates (fat deposits) and the main patterns are traced by ophthalmologists. This paper proposes a fully automated Computer Assisted Evaluation (CAE) system which comprises of a set of algorithms for exudates detection and to classify the different stages of Diabetics Retinopathy, which are identified as either normal or mild or moderate or severe. Experimental validation is performed on a real fundus retinal image database. The segmentation of exudates is achieved using fuzzy C-means clustering and entropy filtering. An optimal set obtained from the statistical textural features (GLCM and GLHM) is extracted from the segmented exudates for classifying the different stages of Diabetics Retinopathy. The different stages of Diabetic Retinopathy are classified using three classifiers such as Back Propagation Neural Network (BPN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). The experimental results show that the SVM classifiers outperformed other classifiers for the examined fundus retinal image dataset. The results obtained confirm that with new a set of texture features, the proposed methodology provides better performance when compared to the other methods available in the literature. These results suggest that our proposed method in this paper can be useful as a diagnostic aid system for Diabetic Retinopathy.

Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME)

2019 ◽  
Vol 64 (3) ◽  
pp. 297-307 ◽  
Author(s):  
Muhammad Noor-ul-huda ◽  
Samabia Tehsin ◽  
Sairam Ahmed ◽  
Fuad A.K. Niazi ◽  
Zeerish Murtaza
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Medical Image ◽  
Vision Loss ◽  
Medical Image Analysis ◽  
Retinal Images ◽  
Diagnostic Systems ◽  
High Blood Glucose ◽  
Department Of Ophthalmology

Abstract Diabetes mellitus is an enduring disease related with significant morbidity and mortality. The main pathogenesis behind this disease is its numerous micro- and macrovascular complications. In developing countries, diabetic retinopathy (DR) is one of the major sources of vision impairment in working age population. DR has been classified into two categories: proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR). NPDR is further classified into mild, moderate and severe, while PDR is further classified into early PDR, high risk PDR and advanced diabetic eye disease. DR is a disease caused due to high blood glucose levels which result in vision loss or permanent blindness. High-level advancements in the field of bio-medical image processing have speeded up the automated process of disease diagnoses and analysis. Much research has been conducted and computerized systems have been designed to detect and analyze retinal diseases through image processing. Similarly, a number of algorithms have been designed to detect and grade DR by analyzing different symptoms including microaneurysms, soft exudates, hard exudates, cotton wool spots, fibrotic bands, neovascularization on disc (NVD), neovascularization elsewhere (NVE), hemorrhages and tractional bands. The visual examination of the retina is a vital test to diagnose DR-related complications. However, all the DR computer-aided diagnostic systems require a standard dataset for the estimation of their efficiency, performance and accuracy. This research presents a benchmark for the evaluation of computer-based DR diagnostic systems. The existing DR benchmarks are small in size and do not cover all the DR stages and categories. The dataset contains 1445 high-quality fundus photographs of retinal images, acquired over 2 years from the records of the patients who presented to the Department of Ophthalmology, Holy Family Hospital, Rawalpindi. This benchmark provides an evaluation platform for medical image analysis researchers. Furthermore, it provides evaluation data for all the stages of DR.

scholarly journals Diabetic Retinopathy Detection System

2021 ◽  
Vol 9 (9) ◽  
pp. 1498-1502
Author(s):  
Manisha Gangesh
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Web Application ◽  
Detection System ◽  
Retinal Images ◽  
Learning Approaches ◽  
Keywords Diabetic Retinopathy ◽  
Machine Learning Model ◽  
User Friendly

Abstract: Diabetic Retinopathy is a diabetes problem that affects the eye. Injury to the blood vessels of the light sensitive tissue inside the rear of the eye (retina) is that the most reason for diabetic retinopathy. To begin with, Diabetic Retinopathy may have no symptoms or just cause minor vision problems. It has the potential to lead to blindness. Machine learning approaches can be used for the early detection of Diabetic Retinopathy. This paper proposes an automated Diabetic Retinopathy detection system that can detect the presence of Diabetic Retinopathy from retinal images. This work uses ResNet50 for the detection and classification of Diabetic Retinopathy. ResNet50 is a type of neural network used as a backbone for many computer-vision tasks. This paper proposes a machine learning model which is developed using ResNet50, then the model will be deployed as a user-friendly web application where the user can upload the retinal images as input to the system then system will detect the presence of Diabetic Retinopathy and classifies it into the stage or class which the particular image belongs to. Keywords: Diabetic Retinopathy, ResNet50, Proliferative diabetic retinopathy, non-proliferative diabetic retinopathy.

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