Phase Of Diabetic Retinopathy Research Articles

Deep Learning Approach for Diabetic Retinopathy Detection

One of the main causes of diabetic vision loss in the active population is diabetic retinopathy, which is more commonly abbreviated as DR. This condition is caused by changes in the vessels that line the retina. One of the most serious entanglements of diabetes, diabetic retinopathy can bring about long-lasting visual impairment whenever left untreated. One of the basic troubles is early distinguishing proof, which is fundamental for treatment accomplishment. One critical ensnarement is Diabetic Retinopathy. Diabetic retinopathy is an eye infirmity caused because of delayed diabetes. On the off chance that unacknowledged or incapable treatment can prompt irreversible vision misfortune. This illness can be recognized by examining variety fundus photos of the human retina. Our work evaluates in profound learning field which is utilized for the discovery and class isolation of diabetic retinopathy into parallel or multiclass and examine them in view of various measurement estimates like accuracy, review, precision. The characterization of diabetic retinopathy includes various advances like pre-handling of picture; include extraction, dimensionality decrease and order of exudates, woolen spot, hemorrhages and microaneurysm. The mechanized methods for Diabetic Retinopathy affirmation are versatile for cost and time decline and are more able over manual examination. Significant Learning technique performs PC upheld clinical end. This paper is an undertaking toward finding a modified response for Diabetic Retinopathy affliction in early phase unfortunately; the particular unmistakable evidence of the diabetic retinopathy stage is broadly fascinating and requires ace human interpretation of fundus pictures. Improvement of the revelation step is fundamental and can help an enormous number of people. Convolutional cerebrum associations (CNN) have been really applied in various adjoining subjects, and for examination of diabetic retinopathy itself. With the right treatment, numerous issues brought about by DR can be kept away from. Since the diabetic retinopathy progresses through a couple of stages, it is really trying for diabetic retinopathy acknowledgment and exploring of such pictures is a drawn-out manual connection. In this work, we are searching for a technique that consequently isolates a picture into different phases of diabetic retinopathy. This errand uses convolutional mind association (CNNs) power for DR area and usages an unreservedly open dataset of Aptos competition open on Kaggle for setting up these models. Each of the 3663 high-quality fundus images in the dataset was approximately 4000 * 2000. The calculation can order the given picture into various DR phases toward the end. In this undertaking we are using Convolutional Cerebrum Association computation. Execution and accuracy of the applied computations is discussed and examined.

A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive Jaya Optimizer for Diabetic Retinopathy

Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive Jaya Optimization Algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods.

Review on Diabetic Retinopathy Detection Using Molecular Segmentation In Convolution Neural Network

Abstract: People who suffer from diabetes are at risk of developing blindness since their pancreas doesn't produce enough insulin. Diabetic retinopathy is a condition in which a patient's vision gradually worsens as the disease advances. To better understand how diabetes affects the eye, retinal images captured with a fundal camera can be helpful. This study aims to detect blood vessels, identify hemorrhages, and classify different phases of diabetic retinopathy into normal, mild, and non-proliferative diabetic retinopathy (NPDR). When it comes to classifying the various stages of diabetes-related retinopathy, the retinal picture is used as a starting point. Using the contrast between the blood vessels and the surrounding background, the retinal vascular is segmented.. Density analysis and bounding box approaches were used to identify potential hemorrhage sites. According to blood vessel and hemorrhage area and perimeter, Random Forests technique was used to finally classify various phases of eye disease. Keywords: Retina, blood vessel, hemorrhages, classification, diabetic retinopathy.

Effect of prorenin peptide vaccine on the early phase of diabetic retinopathy in a murine model of type 2 diabetes.

Prorenin is viewed as an ideal target molecule in the prevention of diabetic retinopathy. However, no drugs are available for inhibiting activation of prorenin. Here, we tested the effect of a prorenin peptide vaccine (VP) in the retina of a murine model of type 2 diabetes (T2D). To choose the optimal vaccine, we selected three different epitopes of the prorenin prosegment (E1, E2, and E3) and conjugated them to keyhole limpet hemocyanin (KLH). We injected C57BL/6J mice twice with KLH only (as a control vaccine), E1 conjugated with KLH (E1-KLH), E2-KLH, or E3-KLH and compared antibody titers. E2-KLH showed the highest antibody titer and specific immunoreactivity of anti-sera against prorenin, so we used E2-KLH as VP. Then, we administered injections to the non-diabetic db/m and diabetic db/db mice, as follows: db/m + KLH, db/db + KLH, and db/db + VP. Retinal blood flow measurement with laser speckle flowgraphy showed that the impaired retinal circulation response to both flicker light and systemic hyperoxia in db/db mice improved with VP. Furthermore, the prolonged implicit time of b-wave and oscillatory potentials in electroretinography was prevented, and immunohistochemical analysis showed reduced microglial activation, gliosis, and vascular leakage. The enzyme-linked immunosorbent spot assay confirmed vaccinated mice had no auto-immune response against prorenin itself. The present data suggest that vaccination against prorenin is an effective and safe measure against the early pathological changes of diabetic retinopathy in T2D.

Short-and Long-Term Expression of Vegf: A Temporal Regulation of a Key Factor in Diabetic Retinopathy.

To investigate the role of vascular endothelial growth factor (VEGF) at different phases of diabetic retinopathy (DR), we assessed the retinal protein expression of VEGF-A164 (corresponding to the VEGF165 isoform present in humans, which is the predominant member implicated in vascular hyperpermeability and proliferation), HIF-1α and PKCβ/HuR pathway in Ins2 Akita (diabetic) mice at different ages. We used C57BL6J mice (WT) at different ages as control. Retina status, in terms of tissue morphology and neovascularization, was monitored in vivo at different time points by optical coherence tomography (OCT) and fluorescein angiography (FA), respectively. The results showed that VEGF-A164 protein expression increased along time to become significantly elevated (p < 0.05) at 9 and 46 weeks of age compared to WT mice. The HIF-1α protein level was significantly (p < 0.05) increased at 9 weeks of age, while PKCβII and HuR protein levels were increased at 46 weeks of age compared to WT mice. The thickness of retinal nerve fiber layer as measured by OCT was decreased in Ins2 Akita mice at 9 and 46 weeks of age, while no difference in the retinal vasculature were observed by FA. The present findings show that the retina of the diabetic Ins2 Akita mice, as expected for mice, does not develop proliferative retinopathy even after 46 weeks. However, diabetic Ins2 Akita mice recapitulate the same evolution of patients with DR in terms of both retinal neurodegeneration and pro-angiogenic shift, this latter indicated by the progressive protein expression of the pro-angiogenic isoform VEGF-A164, which can be sustained by the PKCβII/HuR pathway acting at post-transcriptional level. In agreement with this last concept, this rise in VEGF-A164 protein is not paralleled by an increment of the corresponding transcript. Nevertheless, the observed increase in HIF-1α at 9 weeks indicates that this transcription factor may favor, in the early phase of the disease, the transcription of other isoforms, possibly neuroprotective, in the attempt to counteract the neurodegenerative effects of VEGF-A164. The time-dependent VEGF-A164 expression in the retina of diabetic Ins2 Akita mice suggests that pharmacological intervention in DR might be chosen, among other reasons, on the basis of the specific stages of the pathology in order to pursue the best clinical outcome.

Модель прогнозування тяжкості діабетичної ретинопатії, отримана на основі ендотеліальної дисфункції та маркерів гіпоксії

Актуальність. Діабетична ретинопатія — специфічне мікросудинне ускладнення цукрового діабету. Вона залишається провідною причиною втрати зору в працездатного дорослого населення. У пацієнтів із тяжким ступенем діабетичної ретинопатії спостерігається зниження якості життя та погіршення фізичного, емоційного й соціального благополуччя. До того ж на цю когорту пацієнтів витрачається більше ресурсів системи охорони здоров’я. Розробка математичних моделей прогнозування дозволяє більш різнобічно аналізувати експериментальні та клінічні дані. Мета дослідження: розробка моделі прогнозування тяжкості діабетичної ретинопатії на основі маркерів ендотеліальної дисфункції та гіпоксії. Матеріали та методи. Використовували стрептозотоцинову модель цукрового діабету 2-го типу. Визначали рівні фактора Віллебранда, ендотеліну-1, 2,3-дифосфогліцерату, що використовували як змінні. Перехід непроліферативної фази діабетичної ретинопатії в проліферативну на 180-ту добу експерименту підтверджували гістологічно. Результати. Розроблена математична модель для прогнозування досліджуваного патологічного стану на основі біохімічних аналізів крові на ранніх етапах експерименту. Доведена інформативність маркерів ендотеліальної дисфункції, фактора Віллебранда й ендотеліну-1, у прогнозуванні переходу непроліферативної діабетичної ретинопатії в проліферативну. Встановлено значущість визначення рівня 2,3-дифосфогліцерату в еритроцитах разом iз зазначеними вище маркерами функціонального статусу ендотелію на 30-ту добу для прогнозування подальшого перебігу хвороби. Висновки. Доведено значущість показника 2,3-дифосфогліцерату в еритроцитах і зазначених маркерів функціонального статусу ендотелію на 30-ту добу для прогнозування подальшого перебігу досліджуваного патологічного процесу.

Automated Diabetic Retinopathy Detection using CS Based SVM Techniques

Diabetes is one of the metabolic maladies where a patient has high glucose either brought about by body inability to create enough insulin or the cells inability to react to deliver insulin. This ceaseless ailment may prompt long haul inconveniences and death. It can cause high danger of kidney disappointment, nervous system harm, visual impairment and coronary illness. During the ongoing years there have been numerous examinations on programmed finding of diabetic retinopathy utilizing a few features and techniques. In this work at first the color fundus image can be utilized for processing, methods, for example, Median filtering, Morphological transformation, or Histogram equalization used to improve the nature of the image. Then to detect microaneurysms, blood vessels and optic disc using the techniques like morphological thresholding transformation, and the features are extracted from Grey level Co-occurrence Matrix (GLCM), Gabor Feature extraction and Linear Binary Pattern (LBD).At long last, for classify the various phases of diabetic retinopathy, SVM (support Vector Machine) Algorithm will be utilized, the outcomes are optimized by Cuckoo Search (CS) calculation.

Sistem Klasifikasi Tingkat Keparahan Retinopati Diabetik Menggunakan Support Vector Machine

Diabetic retinopathy is a vision disorder disease that can cause damage to the retina of the eye that will have a direct impact on the disruption of vision of the patient. The diabetic retinopathy phase is classified into four types (normal, mild NPDR, moderate NPDR (Non-Proliferative Diabetic Retinopathy), and severe NPDR). Retinal of eye data of diabetic retinopathy patients treated from the MESSIDOR database. By applying image processing, the retinal image of the eye in extraction using the area features extraction from the detection of exudate, blood vessels, microaneurysms, and texture feature extraction Gray Level Co-occurrence Matrix. The extracted results classified using the Support Vector Machine method with the Radial Basis Function (RBF) kernel. Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.The results of classification show the best value using 6 statistical features ie, contrast, homogeneity, correlation, energy, entropy and inverse difference moment in the direction of 45 degrees with the RBF kernel. The result of classification research system on 240 data training and 60 data testing yields an average accuracy is 95.93%, the value of specificity is 97.29%, and a sensitivity rating is 91.07%. From the research result, using RBF kernel get the best accuracy result than using kernel polynomial or kernel linear.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Retinal Blood Vessel Test in Young-Onset Diabetic Patients

136 patients with insulin-dependent diabetes mellitus (IDDM) were shown the entoptic blood vessel figure. All patients were examined independently by retinal fluorescein angiography and ophthalmoscopy. 34 patients had no retinopathy, none of them believed to see alterations. 102 patients had different phases of diabetic retinopathy. If there were 1-5 microaneurysms within the 15 degrees meridian surrounding the central fovea, 55% of the patients were able to detect their alterations. If there were 6-20 microaneurysms, 77% of the patients were able to diagnose them. 90% of the patients with an even more severe retinopathy could see their abnormalities. Obviously there are no incorrectly positive results. For intelligent and well-observing patients with IDDM the retinal blood vessel test is a useful method for early detection and self control of diabetic retinopathy. Nevertheless, control of the fundus by an ophthalmologist is still necessary.

The Reactive Phase of Diabetic Retinopathy and the Role of the Reticuloendothelial System

The Reactive Phase of Diabetic Retinopathy and the Role of the Reticuloendothelial System