Diabetic Retinopathy Detection from Fundus Images Using Machine Learning Techniques : A Review

2021 ◽  
Author(s):  
Anoop Balakrishnan Kadan ◽  
Perumal Sankar Subbian
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Machine Learning Techniques ◽  
Fundus Images ◽  
Learning Techniques

scholarly journals A Survey on Diagnosis and Analysis of Diabetic Retinopathy using Feature Selection

2020 ◽  
pp. 170-176
Author(s):  
Amalu Michael ◽  
Deepa S S
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Diabetic Retinopathy ◽  
High Ratio ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diabetic Eye Disease ◽  
Selection Mechanisms ◽  
Feature Selection Techniques

Diabetic retinopathy is one of the common forms of diabetic eye disease. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal vessels. Machine learning may be a broad multidisciplinary field that has its roots in statistics, algebra, data processing, and information analytics, etc. Machine learning is used to discover patterns from medical data and provide an efficient way to predict diseases.ML is an application of artificial intelligence it collects information from training data. There are several machine learning techniques are used for the diagnosis of diabetic retinopathy. This paper mainly focuses on the survey of such techniques and also various feature selection mechanisms. This study provides the basic categorization of feature selection techniques and discussing their use.

scholarly journals Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

2021 ◽  
Author(s):  
Mehmet Akif Cifci
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
The Other ◽  
Machine Learning Techniques ◽  
Image Processing Techniques ◽  
Learning Techniques ◽  
Fundus Photographs ◽  
Processing Techniques ◽  
Time And Energy

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

scholarly journals Diabetic retinopathy detection and classification using capsule networks

2021 ◽  
Author(s):  
G. Kalyani ◽  
B. Janakiramaiah ◽  
A. Karuna ◽  
L. V. Narasimha Prasad
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Good Prognosis ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Specific Class ◽  
Fundus Images ◽  
The People ◽  
Recent Developments

AbstractNowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

Prediction of Severity of Non Proliferated Diabetic Retinopathy Using Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4219-4222
Author(s):  
ManjulaSri Rayudu ◽  
Srujana Pendam ◽  
Srilaxmi Dasari
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Diversity Index ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant ◽  
Learning Techniques

All the patients of Type1 and more than 60% of Type2 Diabetes suffer from Diabetic Retinopathy (DR). Diabetic retinopathy causes damage to retina of eye and slowly leads to complete vision loss. The longer the patients are suffering from diabetes the probability of presence of DR is more. Hence diabetic retinopathy is to be identified in early stage to avoid blindness. The objective of this research work is to predict the severity of diabetic retinopathy (Non Proliferated) using machine learning techniques. Proliferated diabetic retinopathy (later stage) is characterized by neovasculature in the retinal veins and is the final stage. Non proliferated DR (earlier stage) is identified by any of the abnormalities out of microaneurysms, Hard exudates and hemorrhages. Then Machine learning techniques are employed to identify the class of DR. The following Classification and regression techniques are employed for categorizing the DR: Gini Diversity Index method, Linear discriminant analysis, Ensemble method with bagged and boosted trees, K-Nearest Neighbor, and Support Vector Machine classification methods. 89 images from DRIVE database (DiaRet DB1) are classified using the machine learning techniques cited above. It is observed the maximum accuracy is achieved as 88.8% with Linear SVM classifier.

scholarly journals CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis

2020 ◽  
pp. 164-172 ◽  
Author(s):  
Adekanmi Adeyinka Adegun ◽  
Roseline Oluwaseun Ogundokun ◽  
Marion Olubunmi Adebiyi ◽  
Emmanuel Oluwatobi Asani
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Skin Cancer ◽  
Human Factor ◽  
Medical Images ◽  
Region Of Interest ◽  
Machine Learning Techniques ◽  
Age Related ◽  
Learning Techniques

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

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