Classification of diabetic retinopathy using textural features in retinal color fundus image

2017 ◽  
Author(s):  
A.G. Anantha Padmanabha ◽  
M Abhishek Appaji ◽  
Mukesh Prasad ◽  
Haiyan Lu ◽  
Sudhanshu Joshi
Keyword(s):  
Diabetic Retinopathy ◽  
Textural Features ◽  
Fundus Image ◽  
Color Fundus Image

A SEQUENTIAL LEARNING METHOD FOR DETECTION AND CLASSIFICATION OF EXUDATES IN RETINAL IMAGES TO ASSESS DIABETIC RETINOPATHY

2014 ◽  
Vol 22 (03) ◽  
pp. 413-428 ◽  
Author(s):  
M. PONNIBALA ◽  
S. VIJAYACHITRA
Keyword(s):  
Diabetic Retinopathy ◽  
Learning Process ◽  
Sequential Learning ◽  
Optic Disk ◽  
Morphological Operations ◽  
Fundus Image ◽  
Cognitive Component ◽  
Color Fundus Image ◽  
Self Adaptive

One of the greatest concerns to the personnel in the current health care sector is the severe progression of diabetes. People can often have diabetes and be completely unaware as the symptoms seem harmless when they are seen on their own. Diabetic retinopathy (DR) is an eye disease that is associated with long-standing diabetes. Retinopathy can occur with all types of diabetes and can lead to blindness if left untreated. The conventional method followed by ophthalmologists is the regular testing of the retina. As this method takes time and energy of the ophthalmologists, a new feature-based automated technique for classification and detection of exudates in color fundus image is proposed in this paper. This method reduces the work of the professionals while examining every fundus image rather than only on abnormal image. The exudates are detected from the color fundus image by applying a few pre-processing techniques that remove the optic disk and similar blood vessels using morphological operations. The pre-processed image was then applied for feature extraction and these features were utilized for classification purpose. In this paper, a novel classification technique such as self-adaptive resource allocation network (SRAN) and meta-cognitive neural network (McNN) classifier is employed for classification of images as exudates, their severity and nonexudates. SRAN classifier makes use of self-adaptive thresholds to choose the appropriate training samples and removes the redundant samples to prevent over-training. These selected samples are availed to improve the classification performance. McNN classifier employs human-like meta-cognition to regulate the sequential learning process. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. It is therefore evident that the implementation of human meta-cognitive learning principle improves efficient learning.

scholarly journals A Generic Study on Diabetic Retinopathy Detection

2021 ◽  
Vol 12 (3) ◽  
pp. 4274-4783
Author(s):  
Akash Bhakat Et.al
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Automated Detection ◽  
Fundus Image ◽  
Patients With Diabetes ◽  
Color Fundus Image ◽  
Flow Through ◽  
Sugar Levels ◽  
Very High ◽  
Growing Population

Diabetic Retinopathy is one of a major cause of visual defects in the growing population which affects the light perception part of the retina. It affects both types of diabetes mellitus. It occurs when high blood sugar levels damage the blood vessels in retina causing them to swell and leak or stop blood flow through them. It starts with no or mild vision problems and can eventually cause blindness if not treated.With the advancements in technology, automated detection and analysis of the stage of Diabetic Retinopathy will help in early detection and treatment. Almost 75% of the patients with diabetes have the risk of being affected by this disease. With early detection this disease can be prevented. Currently DR detection is a traditional and manual, time-consuming process. It requires a trained technician to analyze the color fundus image of retina.With the ever growing population, DR detection is very high in demand to prevent blindness. In this paper we aim to review the existing methodologies and techniques for detection. Also a system for the detection of the 4 stages of DR is proposed.

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.

scholarly journals Automated Detection and Classification of Diabetic Retinopathy using ConvNets

2021 ◽  
Vol 9 (10) ◽  
pp. 1360-1369
Author(s):  
Aavani B
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Confusion Matrix ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Data Set ◽  
Permanent Loss ◽  
Process Computer ◽  
Degree Classification

Abstract: Diabetic retinopathy is the leading cause of blindness in diabetic patients. Screening of diabetic retinopathy using fundus image is the most effective way. As the time increases this DR leads to permanent loss of vision. At present, Diabetic retinopathy is still being treated by hand by an ophthalmologist which is a time-consuming process. Computer aided and fully automatic diagnosis of DR plays an important role in now a day. Data-set containing a collection of fundus images of different severity scale is used to analyze the fundus image of DR patients. Here the deep neural network model is trained by using this fundus image and five-degree classification task is performed. We were able to produce an sensitivity of 90%. Keywords: Confusion matrix, Deep convolutional Neural Network, Diabetic Retinopathy, Fundus image, OCT

Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy

2021 ◽  
Vol 68 ◽  
pp. 102600
Author(s):  
Sraddha Das ◽  
Krity Kharbanda ◽  
Suchetha M ◽  
Rajiv Raman ◽  
Edwin Dhas D
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Image Features ◽  
Fundus Image
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