DRD-Net: Diabetic Retinopathy Diagnosis Using A Hybrid Convolutional Neural Network

Diabetic retinopathy is the predominant cause of visual impairment in diabetes patients. The early detection process can prevent diabetes patients from severe situations. The progression of diabetic retinopathy is determined by analyzing the fundus images, thus determining whether they are affected by exudates or not. The manual detection process is laborious and requires more time and there is a possibility of wrong predictions. Therefore, this research focuses on developing an automated decision-making system. To predict the existence of exudates in fundus images, we developed a novel technique named a hybrid convolutional neural network-recurrent neural network along with the artificial humming bird optimization (HCNNRNN-AHB) approach. The proposed HCNNRNN-AHB technique effectively detects and classifies the fundus image into two categories namely exudates and non-exudates. Before the classification process, the optic discs are removed to prevent false alarms using Hough transform. Then, to differentiate the exudates and non-exudates, color and texture features are extracted from the fundus images. The classification process is then performed using the HCNNRNN-AHB approach which is the combination of CNN and RNN frameworks along with the AHB optimization algorithm. The AHB algorithm is introduced with this framework to optimize the parameters of CNN and RNN thereby enhancing the prediction accuracy of the model. Finally, the simulation results are performed to analyze the effectiveness of the proposed method using different performance metrics such as accuracy, sensitivity, specificity, F-score, and area under curve score. The analytic result reveals that the proposed HCNNRNN-AHB approach achieves a greater prediction and classification accuracy of about 97.4%.

Diabetic-caused retinopathy is a disease that damages the blood vessels that are present in the part of retina of the eyes and might lead to permanent blindness in people who have diabetes. Ophthalmologists use the fundus images of the eyes, which are the patients identify this disease. However, manual detection of this abnormality is very time taking, cost-consuming, and has a higher probability of resulting in errors. Thus, to prevent this, a Deep Learning model is proposed to detect Diabetic caused retinopathy by using high-resolution fundus images which as a result decreases the chances of misdiagnosis. Initially, fundus images are pre-processed by using the Contrast limited adaptive histogram equalization (CLAHE) model and histogram-based segmentation model. In the next step, features from the pre-processed images are extracted. However, it becomes very difficult for the individual models to identify the behind features that are complicated and those can only classify Diabetic Retinopathy's various stages at low accuracy. Thus, to resolve this an ensemble of three deep Convolution Neural Network (CNN) models (Inceptionv3, Xception, and DenseNet121) is developed which will encode the rich features and improve the classification for different stages of DR like normal, mild, moderate, severe and PDR (Proliferative Diabetic Retinopathy). The results show that the proposed ensemble model detects all the Diabetic Retinopathy stages, not like the individual models and performs better comparatively. This improvement can thus reduce time, effort, and labor in healthcare services and will also increase the final decision accuracy.

Diabetes is one of the most common illnesses, and diabetes causes Diabetic Retinopathy (DR), which can lead to vision problems and blindness if not detected early. Early detection of DR is possible with the help of a computer-aided diagnostic (CAD) system, thus preventing many people from losing their eyesight. In order to classify fundus images into five categories, this article makes use of soft attention as feature refinement in convolutional neural networks (CNN). The proposed framework initially utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) as a pre-processing technique for improving the contrast level of the fundus images and then applied concatenation based CNN architecture coupled with soft attention for feature extraction and classification. The data has been balanced using oversampling so that each DR grade contains equal number of samples during the training process. The experimental results reveal that, unlike current methods, the proposed model detects all the five stages of DR.

The most common causes of blindness in adults worldwide, 2.6% of them are caused by diabetic retinopathy, which is a progressive disease caused by complications of diabetes mellitus. Early detection and prompt treatment help save eyesight. The artificial intelligence technology can provide objective and accurate screening results. Deep learning, especially the Convolutional Neural Network (CNN), which is part of artificial intelligence, has proven successful in solving image problems and is very well used in medical image analysis. CNN works well on large datasets, but it will affect network performance to overfitting in fewer datasets. So to solve the problem of small data, data augmentation techniques can be used. There are various kinds of data augmentation techniques. This study used the CNN method to classify diabetic retinopathy disease, compared several suitable data augmentation techniques for retinal fundus images, and used Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement. This study found that the augmented random zoom technique, together with CLAHE, provided the best accuracy of 98% with 96% sensitivity and 100% specificity.

Diabetic retinopathy (DR) is an eye disease which is caused by high blood sugar and high blood pressure and damage the blood vessels in the back (retina) of the eye. Diabetic retinopathy (DR) is not a reversible process and treatment only sustain vision. The number of ophthalmologists cannot meet the growing demands around the world. This study focuses on the automatic diagnosis of the disease through deep learning. Convolutional neural network (CNN) is more widely used as a deep learning method in medical image analysis. Multiclass image classification of images into non referable DR and referable DR has been done using proposed Convolutional neural network (CNN) model. For multiclass classification problem, the sensitivity, specificity and F2 score value for class 0 (no DR) are 81.75 %, 91.06 % and 81.80 % respectively. Whereas for class 1 (non-severe DR), sensitivity, specificity and F2 score values are 71.28 %, 78.52 % and 70.01 % respectively. Similarly for class 2 (sever DR), sensitivity, specificity and F2 score values are 73.03 %, 93.39 % and 75.08 % respectively. Bayesian optimization has been performed for tuning learning rate and gives optimal learning rate 0.000358 through the optimization process. The customized CNN is then trained using 0.000358 learning rate and then tested on test data The images in dataset have poor contrast and consists of impulse noises. Contrast limited adaptive histogram equalization (CLAHE) method is used to improve the contrast of the image followed by median filter to remove noise present in DR image.

Diabetic retinopathy is a kind of disorder which occurs due to high blood sugar level. This disorder affects retina in many ways. Blood vessels in the retina gets altered . Exudates are secreted, hemorrhages occur, swellings appear in the retina. Diabetic Retinopathy (DR) is the major cause of blindness. Automatic Recognition of DR lesions like Exudates, in digital fundus images can contribute to the diagnosis and screening of this disease. In this approach, an automatic and efficient method to detect the exudates is proposed. The real time retinal images are obtained from a nearby hospital. The retinal images are pre-processed via. Contrast Limited Adaptive Histogram Equalization (CLAHE). The preprocessed colour retinal images are segmented using K-Means Clustering technique. The segmented images establish a dataset of regions. To classify these segmented regions into Exudates and Non-Exudates, a set of features based on colour and texture are extracted. Classification is done using support Vector Machine This method appears promising as it can detect the very small areas of exudates. Keywords - Diabetic Retinopathy, Exudates, fundus image, k means clustering, SVM I. INTRODUCTION The retina is the inner and most important layer of the eye. It is composed of several important anatomical structures which can indicate various diseases. Cardiovascular disease such as stroke and myocardial infarcation can be identified from retinal blood vessels. Diabetic Retinopathy is the common retinal complication associated with diabetes. It is a major cause of blindness in both middle and advanced age groups. The International Diabetes Federation reports that over 50 million people in India have this disease and it is growing rapidly (IDF 2009a) (2). The estimated prevalence of diabetes for all age groups worldwide was 2.8% in 2000 and 4.4% in 2030 meaning that the total number of diabetes patients is forecasted to rise from 171 million in 2000 to 366 million in 2030 (3). Therefore regular screening is the most efficient way of reducing the vision loss. Diabetic Retinopathy is mainly caused by the changes in the blood vessels of the retina due to

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system. Nevertheless, manual examination is sometimes arduous, time-consuming, and prone to errors. Deep learning-based methods have recently demonstrated encouraging results in several areas, such as image categorization and natural language mining. The majority of deep learning techniques developed for medical image analysis rely on convolutional modules to extract the inherent structure of images within a certain local receptive field. Furthermore, transformer-based models have been utilized to tackle medical image processing problems by capitalizing on global connections among distant pixels in the images. Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. This model is designed to accurately identify and classify diabetic retinopathy lesions displayed in fundus images. Furthermore, the vision mamba component incorporates the bidirectional state space method and positional embedding to enable the location sensitivity of visual data samples and meet the conditions for global relationship context. An evaluation of the suggested method was carried out by comparison experiments between state-of-the-art algorithms and the proposed methodology. Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. Hence, the suggested approach may be regarded as a helpful tool for therapeutic processes.

BackgroundWith the diabetes mellitus (DM) prevalence increasing annually, the human grading of retinal images to evaluate DR has posed a substantial burden worldwide. SmartEye is a recently developed fundus image processing and analysis system with lesion quantification function for DR screening. It is sensitive to the lesion area and can automatically identify the lesion position and size. We reported the diabetic retinopathy (DR) grading results of SmartEye versus ophthalmologists in analyzing images captured with non-mydriatic fundus cameras in community healthcare centers, as well as DR lesion quantitative analysis results on different disease stages.MethodsThis is a cross-sectional study. All the fundus images were collected from the Shanghai Diabetic Eye Study in Diabetics (SDES) program from Apr 2016 to Aug 2017. 19,904 fundus images were acquired from 6013 diabetic patients. The grading results of ophthalmologists and SmartEye are compared. Lesion quantification of several images at different DR stages is also presented.ResultsThe sensitivity for diagnosing no DR, mild NPDR (non-proliferative diabetic retinopathy), moderate NPDR, severe NPDR, PDR (proliferative diabetic retinopathy) are 86.19, 83.18, 88.64, 89.59, and 85.02%. The specificity are 63.07, 70.96, 64.16, 70.38, and 74.79%, respectively. The AUC are PDR, 0.80 (0.79, 0.81); severe NPDR, 0.80 (0.79, 0.80); moderate NPDR, 0.77 (0.76, 0.77); and mild NPDR, 0.78 (0.77, 0.79). Lesion quantification results showed that the total hemorrhage area, maximum hemorrhage area, total exudation area, and maximum exudation area increase with DR severity.ConclusionsSmartEye has a high diagnostic accuracy in DR screening program using non-mydriatic fundus cameras. SmartEye quantitative analysis may be an innovative and promising method of DR diagnosis and grading.

It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Early diagnosis can minimise the risk of proliferated diabetic retinopathy, which is the advanced level of this disease, and having higher risk of severe impairment. Therefore, it becomes important to classify DR stages. To this effect, this paper presents a weighted fusion deep learning network (WFDLN) to automatically extract features and classify DR stages from fundus scans. The proposed framework aims to treat the issue of low quality and identify retinopathy symptoms in fundus images. Two channels of fundus images, namely, the contrast-limited adaptive histogram equalization (CLAHE) fundus images and the contrast-enhanced canny edge detection (CECED) fundus images are processed by WFDLN. Fundus-related features of CLAHE images are extracted by fine-tuned Inception V3, whereas the features of CECED fundus images are extracted using fine-tuned VGG-16. Both channels’ outputs are merged in a weighted approach, and softmax classification is used to determine the final recognition result. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method tested on the Messidor dataset reports an accuracy level of 98.5%, sensitivity of 98.9%, and specificity of 98.0%, whereas on the Kaggle dataset, the proposed model reports an accuracy level of 98.0%, sensitivity of 98.7%, and specificity of 97.8%. Compared with other models, our proposed network achieves comparable performance.

Computer based automated system is one of the important diagnostic tools in medical field. Diabetic Retinopathy is an eye disorder in which red lesions due to blood leakages can be spotted on retinal surface. This disease is commonly observed in long term diabetic patients. Ignorance to this disease can result into permanent blindness. Early stage signs of diabetic retinopathy are called as Red lesions viz. microaneurysms and hemorrhages. This paper presents a unique methodology for automatic detection of red lesions in fundus images. Proposed methodology employs modified approach to matched filtering for extraction of retinal vasculature and detection of candidate lesions. Features of all candidate lesions are extracted and are used to train Support Vector Machine classifier. In turn support Vector Machine classifies input image object into lesion or non-lesion category. The method is tested on 89 fundus images from DIARETDB1 database. The proposed algorithm gives performance as sensitivity 96.42%, specificity 100% and accuracy 96.62%.

Diabetic retinopathy (DR) is the most common complication of diabetes mellitus and one of the leading causes of preventable blindness in the adult working population. Visual loss can be prevented from the early stages of DR, when the treatments are effective. Therefore, early diagnosis is paramount. However, DR may be clinically asymptomatic until the advanced stage, when vision is already affected and treatment may become difficult. For this reason, diabetic patients should undergo regular eye examinations through screening programs. Traditionally, DR screening programs are run by trained specialists through visual inspection of the retinal images. However, this manual analysis is time consuming and expensive. With the increasing incidence of diabetes and the limited number of clinicians and sanitary resources, the early detection of DR becomes non-viable. For this reason, computed-aided diagnosis (CAD) systems are required to assist specialists for a fast, reliable diagnosis, allowing to reduce the workload and the associated costs. We hypothesize that the application of novel, automatic algorithms for fundus image analysis could contribute to the early diagnosis of DR. Consequently, the main objective of the present Doctoral Thesis is to study, design and develop novel methods based on the automatic analysis of fundus images to aid in the screening, diagnosis, and treatment of DR. In order to achieve the main goal, we built a private database and used five retinal public databases: DRIMDB, DIARETDB1, DRIVE, Messidor and Kaggle. The stages of fundus image processing covered in this Thesis are: retinal image quality assessment (RIQA), the location of the optic disc (OD) and the fovea, the segmentation of RLs and EXs, and the DR severity grading. RIQA was studied with two different approaches. The first approach was based on the combination of novel, global features. Results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity using the private database. We developed a second approach aimed at RIQA based on deep learning. We achieved 95.29% accuracy with the private database and 99.48% accuracy with the DRIMDB database. The location of the OD and the fovea was performed using a combination of saliency maps. The proposed methods were evaluated over the private database and the public databases DRIVE, DIARETDB1 and Messidor. For the OD, we achieved 100% accuracy for all databases except Messidor (99.50%). As for the fovea location, we also reached 100% accuracy for all databases except Messidor (99.67%). The joint segmentation of RLs and EXs was accomplished by decomposing the fundus image into layers. Results were computed per pixel and per image. Using the private database, 88.34% per-image accuracy (ACCi) was reached for the RL detection and 95.41% ACCi for EX detection. An additional method was proposed for the segmentation of RLs based on superpixels. Evaluating this method with the private database, we obtained 84.45% ACCi. Results were validated using the DIARETDB1 database. Finally, we proposed a deep learning framework for the automatic DR severity grading. The method was based on a novel attention mechanism which performs a separate attention of the dark and the bright structures of the retina. The Kaggle DR detection dataset was used for development and validation. The International Clinical DR Scale was considered, which is made up of 5 DR severity levels. Classification results for all classes achieved 83.70% accuracy and a Quadratic Weighted Kappa of 0.78. The methods proposed in this Doctoral Thesis form a complete, automatic DR screening system, contributing to aid in the early detection of DR. In this way, diabetic patients could receive better attention for their ocular health avoiding vision loss. In addition, the workload of specialists could be relieved while healthcare costs are reduced.

In the recent era, the increasing diabetic ratio effects in producing complex retinal diseases. The early diagnosis will avoid the consequences of diabetic-related complications. So, there is an urge to develop a computerized model for identifying the retinopathy features caused due to diabetes while evading the false positives. The aim of this paper is to identify and segment the retinal features like blood vessels, optic disc in diabetic retinopathy (DR) fundus images in order to remove the non-diabetic retinopathy features which makes the detection of DR features (lesions) easier. And this is done by using a hybrid segmentation algorithm called BINI which combines the model of both binary and Niblack’s thresholding. Initially, the input image is standardized by resizing the image using bi-cubic interpolation area method. Then, the fundus image quality is enhanced using preprocessing techniques like green channel extraction, intensity channel extraction, median filtering, contrast limited adaptive histogram equalization and morphological operations. The preprocessed images are segmented to produce retinal features using the BINI algorithm. It is a novel method which hybrids both binary thresholding and Niblack’s thresholding. The performance of the segmentation methods is evaluated using the validation measures like Rand index (Ri) and Jaccard index (Ji), Precision (Pr), Recall (Rc) and F-Measure (Fm). The proposed method for segmenting the retinal features using BINI thresholding has given an accuracy of about 96.48%; it leads to an accuracy of 100% clear segmentation of the lesions of diabetic retinopathy images.

This project proposes machine learning methods for detecting diabetic retinopathy by using CNN. The classification of retinal lesions from non-lesions using CNN classifiers is examined. Retinal retina images can be evaluated using machine learning-based medical image analysis. The image is pre-processed using the Contrast limited adaptive histogram equalization (CLAHE) filter. CLAHE is used to improve the visibility level of blurry images. The image is segmented using the FCM method to generate an appropriate threshold value for data segmentation. The method of feature extraction is implemented by RLC. The Gray Level Co – occurrence Matrix (GLCM) method is used for extracting statistical skin parameters in a given direction and distance. The use of convolutional neural network (CNN) algorithms has facilitated the early identification of diabetic retinopathy. This project provides excellent specificity and sensitivity for classifying images as with or without diabetic retinopathy. This project is implemented using MATLAB software.

Diabetes is a chronic disease affecting millions of people worldwide, and more than 25% of them have diabetic retinopathy (DR). DR is the leading cause of blindness in adults. DR is usually diagnosed with a biomicroscopic examination of the fundus after pupillary dilation, supplemented by fundus photographs. The challenge lies in the interpretation of these images by a specialist physician. In this work we propose a computer-assisted pipeline for the diagnosis of DR using the convolutional neural network (CNN). This pipeline has three main stages: (i) image preprocessing, (ii) feature extraction and (iii) classification. First, we used the Discrete Wavelet Transform (DWT) method for edge segmentation, and the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve image contrast. In the second and third step we used the ResNet50 convolutional neural network for the detection and classification of five levels of disease severity (0 - No DR, 1 - Mild, 2 - Moderate, 3 - Severe, 4 - Proliferative) on 35 122 images from the publicly available Kaggle database.To analyze the performance of the learning model, different metrics were used to detect different diseases, such as sensitivity, specificity, and the confusion matrix which includes the number of true positive, false positive, true negative and false negative.In conclusion, we achieved 85% accuracy in the validation phase and 81% in the testing phase, demonstrating the reliability of CNNs to identify and automate the diagnosis of diabetic retinopathy using digital fundus images.