scholarly journals Diabetic retinopathy classification using deep convolutional neural network

2021 ◽  
Vol 24 (1) ◽  
pp. 208
Author(s):  
Akshita L. ◽  
Harshul Singhal ◽  
Ishita Dwivedi ◽  
Poonam Ghuli
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Traditional Method ◽  
High Accuracy ◽  
Vision Impairment ◽  
Fundus Images ◽  
Computationally Efficient ◽  
Deep Convolutional Neural Networks ◽  
Deep Convolution Neural Network ◽  
Different Levels

<span>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 result achieved is high given the limitations of the dataset and computational powers.</span>

scholarly journals Intelligent detection method and applied research of diabetic retinopathy based on residual attention network

2021 ◽  
Author(s):  
Moye Yu
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Applied Research ◽  
Detection Method ◽  
High Accuracy ◽  
Fundus Images ◽  
Ocular Complication ◽  
Dilated Convolution ◽  
Imbalance Problem ◽  
Intelligent Detection

Abstract Diabetic Retinopathy (DR) is a late-stage ocular complication of diabetes. Proposing a high-accuracy automatic screening technology of fundus images based on deep learning is of great significance to delay the deterioration of DR. In this paper, we propose an end-to-end framework RAN for DR classification and diagnosis based on the ResNet, attention mechanism and dilated convolution was added to the framework. We implemented experiments on three DR datasets, Kaggle, Messidor and IDRid, analyzed and compared the experimental results. The focal loss function is added to solve the imbalance problem between DR datasets. The results show that the method RAN used mainly improves the results of the basic neural network when using the same dataset. Therefore, by optimizing the basic neural network, the classification and diagnosis effect of DR can be improved.

scholarly journals Classification among Microaneurysms, Exudates, and Lesion free Retinal Regions in the Eye Images using Transfer Learned CNNs

2019 ◽  
Vol 9 (2) ◽  
pp. 5508-5512
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Glucose Level ◽  
Vision Loss ◽  
Early Stage ◽  
Training Data ◽  
Fundus Images ◽  
Diabetic Mellitus ◽  
Start Process

When pancreas fails to secrete sufficient insulin in the human body, the glucose level in blood either becomes too high or too low. This fluctuation in glucose level affects different body organs such as kidney, brain, and eye. When the complications start appearing in the eyes due to Diabetic Mellitus (DM), it is called Diabetic Retinopathy (DR). DR can be categorized in several classes based on the severity, it can be Microaneurysms (ME), Haemorrhages (HE), Hard and Soft Exudates (EX and SE). DR is a slow start process that starts with very mild symptoms, becomes moderate with the time and results in complete vision loss, if not detected on time. Early-stage detection may greatly bolster in vision loss. However, it is impassable to detect the symptoms of DR with naked eyes. Ophthalmologist harbor to the several approaches and algorithm which makes use of different Machine Learning (ML) methods and classifiers to overcome this disease. The burgeoning insistence of Convolutional Neural Network (CNN) and their advancement in extracting features from different fundus images captivate several researchers to strive on it. Transfer Learning (TL) techniques help to use pre-trained CNN on a dataset that has finite training data, especially that in under developing countries. In this work, we propose several CNN architecture along with distinct classifiers which segregate the different lesions (ME and EX) in DR images with very eye-catching accuracies.

scholarly journals Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1083
Author(s):  
Jeyapriya J ◽  
K S Umadevi ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Automated Detection ◽  
Convolution Neural Network ◽  
Retinal Images ◽  
Fundus Images ◽  
Catching Up

The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.

scholarly journals Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 205
Author(s):  
Hassan Tariq ◽  
Muhammad Rashid ◽  
Asfa Javed ◽  
Eeman Zafar ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Deep Convolutional Neural Networks ◽  
Appropriate Treatment ◽  
Long Time ◽  
Computer Based ◽  
Degree Classification ◽  
Eye Exams

Diabetic retinopathy (DR) is a human eye disease that affects people who are suffering from diabetes. It causes damage to their eyes, including vision loss. It is treatable; however, it takes a long time to diagnose and may require many eye exams. Early detection of DR may prevent or delay the vision loss. Therefore, a robust, automatic and computer-based diagnosis of DR is essential. Currently, deep neural networks are being utilized in numerous medical areas to diagnose various diseases. Consequently, deep transfer learning is utilized in this article. We employ five convolutional-neural-network-based designs (AlexNet, GoogleNet, Inception V4, Inception ResNet V2 and ResNeXt-50). A collection of DR pictures is created. Subsequently, the created collections are labeled with an appropriate treatment approach. This automates the diagnosis and assists patients through subsequent therapies. Furthermore, in order to identify the severity of DR retina pictures, we use our own dataset to train deep convolutional neural networks (CNNs). Experimental results reveal that the pre-trained model Se-ResNeXt-50 obtains the best classification accuracy of 97.53% for our dataset out of all pre-trained models. Moreover, we perform five different experiments on each CNN architecture. As a result, a minimum accuracy of 84.01% is achieved for a five-degree classification.

scholarly journals Detection of Diabetic Retinopathy using Convolutional Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 1957-1960
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Data Augmentation ◽  
Medical Condition ◽  
Training Dataset ◽  
Fundus Images ◽  
Processor Unit ◽  
High Level ◽  
Graphics Processor Unit ◽  
Small Dataset

Diabetic Retinopathy is a medical condition in which damage occurs to the retina due to diabetes mellitus. The diagnosis of Diabetic Retinopathy through colored fundus images stand in need of experienced clinicians to identify the presence and significance of many small features, which makes it a time consuming task. In this paper, we propose a CNN based approach to detect Diabetic Retinopathy in fundus images. Data used to train the model is prepocessed by a new segmentation technique using Gabor filters. Due to small dataset, data augmentation is done to get enough data to train the model. Our segmentation model detects intricate features in the fundus images and detect the presence of DR. A high-end Graphics Processor Unit (GPU) is used to train the model efficiently. The publicly available Kaggle Dataset is used to demonstrate impressive results, particularly for a high-level classification task. On the training dataset of 14,650 images, our proposed CNN achieves a specificity of 94% and an accuracy of 69% on 3,660 validation images.

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