Detection of Diabetic Retinopathy from Fundus Images using Deep Learning:A Review (original) (raw)

DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING

IAEME PUBLICATION, 2020

Diabetic Retinopathy (DR) is an ailment that arises in patients suffering from Diabetes for more than 2 decades. This causes blindness due to elevated blood glucose levels. The condition can be cured when it is diagnosed at an early stage. The currently available methods to diagnose Diabetic Retinopathy is Fluorescein Angiography, which is slow and is not available to masses. In this paper we propose a novel solution by incorporating Convolutional Neural Networks (CNN) to detect the presence of Diabetic Retinopathy using the color fundus image of the patient. We have built a Neural Network architecture consisting of two stages of CNN where the first stage detects the presence of DR and the second stage classifies it into four stages. With this architecture, we have achieved an accuracy of 90.32%. The dataset we used to train the Neural Network was taken from a Kaggle competition that consisted of 3,662 color fundus images.

IJERT-Detection of Diabetic Retinopathy using Deep Learning

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/detection-of-diabetic-retinopathy-using-deep-learning https://www.ijert.org/research/detection-of-diabetic-retinopathy-using-deep-learning-IJERTV10IS050333.pdf Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and cost-consuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, deep learning has become one of the most common techniques that has achieved better performance in many areas, especially in medical image analysis and classification. Convolutional neural networks are more widely used as a deep learning method in medical image analysis and they are highly effective. For this article, the recent state-ofthe-art methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed. Furthermore, the DR available datasets for the color fundus retina have been reviewed. Difference challenging issues that require more investigation are also discussed. 1.

Detection of Diabetic Retinopathy using deep Convolutional Neural Network

A recent development in the state-of-art technology machine learning plays a vital role in the image processing applications such as biomedical, satellite image processing, Artificial Intelligence such as object identification and recognition and so on. Severity of the diabetic retinopathy disease is based on a presence of micro aneurysms, exudates, neovascularization, Haemorrhages. The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is a manual and time-consuming process. In our approach, we trained a deep Convolutional Neural Network model on a large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy. Amongst other supervised algorithms involved, proposed solution is to find a better and optimized way to classifying the fundus image with little pre-processing techniques. Our proposed architecture deployed with dropout layer techniques yields around 94-96 percent accuracy.

Diabetic Retinopathy Improved Detection Using Deep Learning

Applied Sciences

Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic r...

DIABETIC RETINOPATHY DETECTION USING DEEP NEURAL NETWORK

IJCSMC, 2019

Diabetic Retinopathy (die-uh-BET-ik ret-ih-Nop-uh-thee) is a diabetic complication that effects eyes. It is caused by damage to the blood vessels of the light-sensitive tissues at the back of eye (retina). The condition can developed in anyone who has type 1 or type 2 diabetes. This paper focus on a desktop application that will help you to the identification of diabetic retinopathy. The screening occur in real time. The application can be developed using a tensor flow deep neural network architecture. Here it is trained and tested more than thousands of images. During the creation of deep neural network we will create five layers, 2 pool layer and 2 convolution layer and one fc layer. Fc layers are used to detect specific global configurations of the features detected by the lower layers in the net. In this model there are two options for screening that are one for image screening and one for real time screening. For this desktop application there is no need of internet connection for its working and it can be used as an easy manner.

Detection of Diabetic Retinopathy Using Deep Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Diabetic retinopathy is one of the most dangerous complications of diabetes, leading to permanent blindness if left untreated. One of the major challenges is early detection, which is very important for the success of treatment. Unfortunately, accurate identification of the stage of diabetic retinopathy is notoriously tricky and requires expert human interpretation of fundus images. Simplifying the detection step is essential and can help millions of people. Convolutional Neural Networks (CNNs) have been successfully used in many neighboring subjects and for the diagnosis of diabetic retinopathy itself. However, the high cost of large annotated datasets as well as inconsistencies between different clinicians hinders the implementation of these methods. In this paper, we propose an automatic method based on deep learning to detect the stage of diabetic retinopathy using a single human fundus image. In addition, we propose a multi-stage transfer learning approach that uses similar datasets with different labels. The presented method can be used as a screening method for the early detection of diabetic retinopathy with a sensitivity and specificity of 0.99 and is ranked 54 out of 2943 competing methods (quadratic weighted kappa score 0.925466) on the APTOS 2019 Blindness Detection Dataset (13,000 images). I.

Diabetic retinopathy detection through deep learning techniques: A review

Informatics in Medicine Unlocked , 2020

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and costconsuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, deep learning has become one of the most common techniques that has achieved better performance in many areas, especially in medical image analysis and classification. Convolutional neural networks are more widely used as a deep learning method in medical image analysis and they are highly effective. For this article, the recent state-of-theart methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed. Furthermore, the DR available datasets for the color fundus retina have been reviewed. Difference challenging issues that require more investigation are also discussed.

Diabetic Retinopathy Detection and Grading using Deep learning

MJEER, 2022

One of the complications of diabetes disease is diabetic retinopathy (DR). Diabetic patients may suffer from total loss of sight. That's if it is not detected and medicated early enough. The early detection of DR is very important during funds screening on a regular basis. Detection and grading of DR are difficult because most fundus images suffer from undersaturation and noise. This paper proposes a new enhancement process as a solution to the poor quality of fundus images. It also proposes two architectures for convolutional neural network (CNN) models. The first one is the binary classifier of DR images into normal and abnormal. The second CNN architecture to classify the severity grades of DR. In this study, we also utilized different pre-trained convolutional neural network models to show the impact on the performance of the use of transfer learning from pre-trained CNN models vs newly defined architectures. The pre-trained CNN models and the two new proposed CNN models are tested using Messidor1, Messidor2, and Kaggle EyePACS datasets. The proposed binary classifier model results in F1-scores of 0.9387, 0.9629, and 0.9430 on the Messidor-1, Messidor-2, and EyePACS datasets, respectively. The proposed second model classifies the five grades with an F1-score of 0.9133, 0.9226, and 0.9393 on the Messidor1, Messidor2, and Kaggle EyePACS datasets, respectively. The new proposed CNN model proved its reliability and efficiency in detecting DR and classifying severity grades of DR in fundus images. Preprocessing techniques enhanced the performance by 10.83% of accuracy and 0.13037 in AUC using the binary model.

Early Detection of Diabetic Retinopathy by Using Deep Learning Neural Network

International Journal of Engineering & Technology

This project presents a method to detect diabetic retinopathy on the fundus images by using deep learning neural network. Alexnet Convolution Neural Network (CNN) has been used in the project to ease the process of neural learning. The data set used were retrieved from MESSIDOR database and it contains 1200 pieces of fundus images. The images were filtered based on the project needed. There were 580 pieces of images types .tif has been used after filtered and those pictures were divided into 2, which is Exudates images and Normal images. On the training and testing session, the 580 mixed of exudates and normal fundus images were divided into 2 sets which is training set and testing set. The result of the training and testing set were merged into a confusion matrix. The result for this project shows that the accuracy of the CNN for training and testing set was 99.3% and 88.3% respectively.

Diabetic retinopathy classification using deep convolutional neural network

Indonesian Journal of Electrical Engineering and Computer Science, 2021

Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence and any seriousness of some subtle features in the fundus images. This process is not very efficient as it takes a lot of time and resources. DR testing of all the patients, a lot of which are undiagnosed or untreated, is a big task due to the inefficiency of the traditional method. This paper was written with the aim to propose a classification system based on an efficient deep convolution neural network (DCNN) model which is computationally efficient. Amongst other supervised algorithms involved, proposed solution is to find a way to efficiently classify the fundus images into 5 different levels of severity. Application of segmentation after the pre-processing and then use of deep convolutional neural networks on the dataset results in a high accuracy of 91.52%. The r...