Diabetic Retinopathy Severity Level Research Articles

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

Diabetic retinopathy prediction based on vision transformer and modified capsule network

Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating the evolution of this disease depends on its early detection as well as identifying the exact levels of this pathology, which is done manually by ophthalmologists. This manual process is very consuming in terms of the time and experience of an expert ophthalmologist, which makes developing an automated method to aid in the diagnosis of diabetic retinopathy an essential and urgent need. In this paper, we aim to propose a new hybrid deep learning method based on a fine-tuning vision transformer and a modified capsule network for automatic diabetic retinopathy severity level prediction. The proposed approach consists of a new range of computer vision operations, including the power law transformation technique and the contrast-limiting adaptive histogram equalization technique in the preprocessing step. While the classification step builds up on a fine-tuning vision transformer, a modified capsule network, and a classification model combined with a classification model, The effectiveness of our approach was evaluated using four datasets, including the APTOS, Messidor-2, DDR, and EyePACS datasets, for the task of severity levels of diabetic retinopathy. We have attained excellent test accuracy scores on the four datasets, respectively: 88.18%, 87.78%, 80.36%, and 78.64%. Comparing our results with the state-of-the-art, we reached a better performance.

Classification of diabetic retinopathy severity level using deep learning

Classification of diabetic retinopathy severity level using deep learning

Disparities Between Teleretinal Imaging Findings and Patient-Reported Diabetic Retinopathy Status and Follow-up Eye Care Interval: A 10-Year Prospective Study.

To assess self-reported awareness of diabetic retinopathy (DR) and concordance of eye examination follow-up compared with findings from concurrent retinal images. We conducted a prospective observational 10-year study of 26,876 consecutive patients with diabetes who underwent retinal imaging during an endocrinology visit. Awareness and concordance were evaluated using questionnaires and retinal imaging. Awareness information and gradable images were available in 25,360 patients (94.3%). Severity of DR by imaging was as follows: no DR (n = 14,317; 56.5%), mild DR (n = 6,805; 26.8%), or vision-threatening DR (vtDR; n = 4,238; 16.7%). In the no, mild, and vtDR groups, 96.7%, 88.5%, and 54.9% of patients, respectively, reported being unaware of any prior DR. When DR was present, reporting no prior DR was associated with shorter diabetes duration, milder DR, last eye examination >1 year before, no dilation, no scheduled appointment, and less specialized provider (all P < 0.001). Among patients with vtDR, 41.2%, 58.1%, and 64.2% did not report being aware of any DR and follow-up was concordant with current DR severity in 66.7%, 41.3%, and 25.4% (P < 0.001) of patients when prior examination was performed by a retinal specialist, nonretinal ophthalmologist, or optometrist (P < 0.001), respectively. Substantial discrepancies exist between DR presence, patient awareness, and concordance of follow-up across all DR severity levels. These discrepancies are present across all eye care provider types, with the magnitude influenced by provider type. Therefore, patient self-report should not be relied upon to reflect DR status. Modification of medical care and education models may be necessary to enhance retention of ophthalmic knowledge in patients with diabetes and ensure accurate communication between all health care providers.

Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol

PurposeTo evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR). DesignMulticenter cross-sectional diagnostic study, conducted at three diabetes care and eye care facilities. ParticipantsA total of 327 individuals with diabetes mellitus (Type 1 or Type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis. MethodsParticipants underwent fundus photographs using a portable retinal camera (Phelcom Eyer). The captured images were automatically analysed by deep learning algorithms RAS (retinal alteration score) and DRAS (diabetic retinopathy alteration score), consisting of convolutional neural networks trained on EyePACS datasets and fine-tuned using datasets of portable device fundus images. The ground truth was the classification of DR corresponding to adjudicated expert reading, performed by three certified ophthalmologists. Main Outcome MeasuresPrimary outcome measures included the sensitivity and specificity of the AI system in detecting DR and/or mtmDR using a single-field, macula-centered fundus photograph for each eye, compared to a rigorous clinical reference standard comprising reading center grading of 2-field imaging protocol using the ICDR severity scale. ResultsOf 327 analysed patients (age 57.0 + 16.8 years, diabetes duration 16.3 + 9.7 years), 307 completed the study protocol. Sensitivity and specificity of the AI system were high in detecting any DR with DRAS (sensitivity: 90.48%; 95% CI, 84.99% – 94.46% and specificity 90.65%; 95% CI, 84.54%-94.93%) and mtmDR with the combination of RAS and DRAS (sensitivity: 90.23%; 95% CI, 83.87%-94.69% and specificity: 85.06%; 95% CI, 78.88%-90.00%). The area under the ROC curve was 0.94 for any DR and 0.89 for mtmDR. ConclusionsThis study showed a high accuracy for the detection of DR in different levels of severity with a single retinal photo per eye in an all-in-one solution, composed of a portable retinal camera powered by AI. Such a strategy holds great potential for increasing coverage rates of screening programs, contributing to tackle avoidable blindness.

RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy.

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new "DR-Insight" dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system's goal is to augment optometrists' expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).

Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images

Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.

SEGAN-BASED LESION SEGMENTATION AND OPTIMIZED RideNN FOR DIABETIC RETINOPATHY CLASSIFICATION

The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR[Formula: see text]GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.

The relationship of diabetic retinopathy severity scales with frequency and surface area of diabetic retinopathy lesions

PurposeTo assess the relationship between qualitative diabetic retinopathy (DR) scales with the precise numbers and surface area of DR lesions within the Early Treatment Diabetic Retinopathy Study (ETDRS) standard seven field (S7F) region on ultrawide-field (UWF) color fundus images.MethodsIn this study, we collected UWF images from adult patients with diabetes. Poor-quality images and eyes with any pathology precluding assessment of DR severity were excluded. The DR lesions were manually segmented. DR severity was graded according to the International Clinical Diabetic Retinopathy (ICDR) and AA protocol by two masked graders within the ETDRS S7F. These lesions’ numbers and surface area were computed and correlated against the DR scores using the Kruskal–Wallis H test. Cohen’s Kappa was performed to determine the agreement between two graders.ResultsOne thousand five hundred and twenty eyes of 869 patients (294 females, 756 right eyes) with a mean age of 58.7 years were included. 47.4% were graded as no DR, 2.2% as mild non-proliferative DR (NPDR), 24.0% as moderate NPDR, 6.3% as severe NPDR, and 20.1% as proliferative DR (PDR). The area and number of DR lesions generally increased as the ICDR level increased up to severe NPDR, but decreased from severe NPDR to PDR. There was perfect intergrader agreement on the DR severity.ConclusionA quantitative approach reveals that DR lesions’ number and area generally correlate with ICDR-based categorical DR severity levels with an increasing trend in the number and area of DR lesions from mild to severe NPDR and a decrease from severe NPDR to PDR.

Associations of Diabetic Retinopathy Severity With High Ambulatory Blood Pressure and Suppressed Serum Renin Levels.

The relationships between serum renin levels, severity of diabetic retinopathy (DR), and 24-hour blood pressure (BP) have not been previously reported. To explore causes for DR and the relationships of 24-hour ambulatory BP, and hormone levels with the severity of DR. The diabetic patients were classified as having no DR, simple DR, or severe DR (preproliferative DR plus proliferative DR) based on funduscopic examination, and we measured 24-hour BP, serum active renin (ARC), aldosterone (SAC), adrenocorticotropic hormone, and cortisol levels in each group. Compared to those with no DR or simple DR, patients with severe DR showed significantly higher 24-hour BPs, including daytime and nighttime systolic and diastolic BP levels, independent of diabetic duration and HbA1c levels. The variability of nighttime systolic BP was greater in patients with severe DR than in those with nonsevere DR, although nocturnal BP reduction was similar between the groups. The ambulatory BPs were significantly inversely associated with ARC. The ARC was significantly lower in severe DR patients than in those with no DR or simple DR (3.2 [1.5-13.6] vs 9.8 [4.6-18.0] pg/mL, P < .05), but there were no differences in SAC in patients taking calcium channel blockers and/or α-blockers. No associations were found between DR severity and other hormone levels. Severe DR was associated with higher 24-hour BPs and suppressed ARC. These findings suggest that mineralocorticoid receptor overactivation may play a role in higher BP levels and severe DR in diabetic patients.

Comparison of diabetic retinopathy severity grading on ETDRS 7-field versus ultrawide-field assessment

ObjectivesTo compare the diabetic retinopathy (DR) severity level determined when considering only the ETDRS 7-field region versus the entire ultrawidefield (UWF) image.MethodsIn this retrospective, cross-sectional study, UWF pseudocolor images were graded on the Eyenuk image viewing, grading, and annotation platform for the severity of DR considering only the regions within the ETDRS 7-fields as well as the entire UWF image using two different protocols: 1) the simple International Classification of Diabetic Retinopathy (ICDR) scale and 2) the more complex DRCR.net Protocol AA grading scale.ResultsA total of 250 eyes from 157 patients were included in this analysis. Six eyes (2.4%) demonstrated a discrepancy in severity level between the ETDRS 7-field region and the entire UWF image when using the ICDR classification system. The discrepancies were due to the presence of lesions [intraretinal haemorrhage (n = 2), neovascular disease (n = 4)] in the peripheral fields which were not identified in the ETDRS 7-fields. Fourteen eyes (5.6%) had a discrepancy in severity level between the ETDRS 7-field region and the entire UWF image when using the ETDRS DRSS Protocol AA grading scale. The discrepancies were due to the presence of a higher level of disease [intraretinal haemorrhage (n = 4), neovascularization (n = 4), preretinal haemorrhage (n = 2), scatter laser scars (n = 4)] in the peripheral fields.ConclusionAlthough considering regions outside of the ETDRS 7-fields altered the DR severity level assessment in <5% of cases in this cohort, significant and potentially vision-threatening lesions including neovascularization and preretinal haemorrhage were identified in these peripheral regions. This highlights the importance of evaluating the entire UWF region when assessing patients with diabetic retinopathy.

An Efficient DenseNet for Diabetic Retinopathy Screening

This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%.

Diabetic Retinopathy Prediction Based on Wavelet Decomposition and Modified Capsule Network.

Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pathology, on top of the detection of its specific stage. Furthermore, the early detection of diabetic retinopathy and the follow-up of the patient's condition remains an arduous task, whether for an experienced expert ophthalmologist or a computer-aided diagnosis technician. In this paper, we aim to propose a new automatic diabetic retinopathy severity level detection method. The proposed approach merges the pyramid hierarchy of the discrete wavelet transform of the retina fundus image with the modified capsule network and the modified inception block proposed, in addition to a new deep hybrid model that concatenates the inception block with capsule networks. The performance of our proposed approach has been validated on the APTOS dataset, as it achieved a high training accuracy of 97.71% and a high testing accuracy score of 86.54%, which is considered one of the best scores achieved in this field using the same dataset.

Choroidal vascularity index (CVI)-Net-based automatic assessment of diabetic retinopathy severity using CVI in optical coherence tomography images.

A deep learning model called choroidal vascularity index (CVI)-Net is proposed to automatically segment the choroid layer and its vessels in overall optical coherence tomography (OCT) scans. Clinical parameters are then automatically quantified to determine structural and vascular changes in the choroid with the progression of diabetic retinopathy (DR) severity. The study includes 65 eyes consisting of 34 with proliferative DR (PDR), 17 with nonproliferative DR (NPDR), and 14 healthy controls from two OCT systems. On a dataset of 396 OCT B-scan images with manually annotated ground truths, overall Dice coefficients of 96.6 ± 1.5 and 89.1 ± 3.1 are obtained by CVI-Net for the choroid layer and vessel segmentation, respectively. The mean CVI values among the normal, NPDR, and PDR groups are consistent with reported outcomes. Statistical results indicate that CVI shows a significant negative correlation with DR severity level, and this correlation is independent of changes in other physiological parameters.

Enhanced retinal image captioning system for identifying and differentiating severity levels of diabetic retinopathy

The objective of this research was to formulate a clinical decision support framework leveraging AI towards utilizing retinal fundus images for the identification and categorization of the four distinct stages of diabetic retinopathy, namely proliferative, severe, moderate, and mild. The devised system architecture integrated Long Short-Term Networks (LSTM), Generative Adversarial Networks (GAN), and pre-trained convolutional neural network (CNN) models. Following an exhaustive performance analysis, the most optimal image captioning model was identified and recommended to ophthalmologists for the purpose of identifying and categorizing diabetic retinopathy. Notably, the results revealed that employing ResNet50 with LSTM, in conjunction with enhanced retinal images, yielded superior accuracy of 0.975. The proposed methodology holds transformative potential for the realm of diabetic retinopathy diagnosis and classification, facilitating early detection and intervention to mitigate vision loss in individuals affected by diabetes.

Detection of five severity levels of diabetic retinopathy using ensemble deep learning model

Detection of five severity levels of diabetic retinopathy using ensemble deep learning model

Tortuosity of branch retinal artery is more associated with the genesis and progress of diabetic retinopathy.

ObjectiveThe purpose of this study is to investigate the potential of using the tortuosity of branch retinal artery as a more promising indicator for early detection and accurate assessment of diabetic retinopathy (DR).Design and methodThe diagnoses, consisting of whether DR or not as well as DR severity, were given by ophthalmologists upon the assessment of those fundus images from 495 diabetic patients. Meanwhile, benefiting from those good contrast and high optical resolution fundus images taken by confocal scanning laser ophthalmoscope, the branch arteries, branch veins, main arteries and main veins in retina can be segmented independently, and the tortuosity values of them were further extracted to investigate their potential correlations with DR genesis and progress based on one-way ANOVA test.ResultsFor both two comparisons, i.e., between non-DR group and DR group as well as among groups with different DR severity levels, larger tortuosity increments were always observed in retinal arteries and the increments in branch retinal vessels were even larger. Furthermore, it was newly found that branch arterial tortuosity was significantly associated with both DR genesis (p=0.030) and DR progress (p<0.001).ConclusionBased on this cohort study of 495 diabetic patients without DR and with different DR severity, the branch arterial tortuosity has been found to be more closely associated with DR genesis as well as DR progress. Therefore, the branch arterial tortuosity is expected to be a more direct and specific indicator for early detection of DR as well as accurate assessment of DR severity, which can further guide timely and rational management of DR to prevent from visual impairment or even blindness resulting from DR.

Retinal Transcriptome and Cellular Landscape in Relation to the Progression of Diabetic Retinopathy.

PurposePrevious studies that identify putative genes associated with diabetic retinopathy are only focusing on specific clinical stages, thus resulting genes are not necessarily reflective of disease progression.This study identified genes associated with the severity level of diabetic retinopathy using the likelihood-ratio test (LRT) and ordinal logistic regression (OLR) model, as well as to profile immune and retinal cell landscape in progressive diabetic retinopathy using a machine learning deconvolution approach.MethodsThis study used a published transcriptomic dataset (GSE160306) from macular regions of donors with different degrees of diabetic retinopathy (10 healthy controls, 10 cases of diabetes, 9 cases of nonproliferative diabetic retinopathy, and 10 cases of proliferative diabetic retinopathy or combined with diabetic macular edema). LRT and OLR models were applied to identify severity-associated genes. In addition, CIBERSORTx was used to estimate proportional changes of immune and retinal cells in progressive diabetic retinopathy.ResultsBy controlling for gender and age using LRT and OLR, 50 genes were identified to be significantly increased in expression with the severity of diabetic retinopathy. Functional enrichment analyses suggested these severity-associated genes are related to inflammation and immune responses. CCND1 and FCGR2B are further identified as key regulators to interact with many other severity-associated genes and are crucial to inflammation. Deconvolution analyses demonstrated that the proportions of memory B cells, M2 macrophages, and Müller glia were significantly increased with the progression of diabetic retinopathy.ConclusionsThese findings demonstrate that deep analyses of transcriptomic data can advance our understanding of progressive ocular diseases, such as diabetic retinopathy, by applying LRT and OLR models as well as bulk gene expression deconvolution.

Study of corneal endothelial cells in diabetic patients

Background: The cornea is the anterior transparent part of the eye. In addition to its optical and refractive function, it is an important protective structure.Aim: The aim of this study was to assess corneal endothelium (counts, morphology and structure) as well as corneal thickness of Type 2 diabetic participants.Setting: This is a hospital-based case–control study and was carried out at Ibn Al Haitham tertiary eye hospital in Baghdad, Iraq.Methods: The sample size was 240 eyes of 120 diabetic participants and 120 healthy participants. Non-contact specular microscopy was utilised to evaluate corneal endothelial cells, including endothelial cell density (ECD), coefficient of variation in cell area (CV), hexagonality (HEX) of cells as well as central corneal thickness (CCT).Results: The ECD was lower in the diabetic corneas (2584.87 ± 259.15 cell/mm2) compared with the healthy corneas (2717.56 ± 289.67 cell/mm2) (p = 0.017, statistically significant). Coefficient of variance (CV) was greater in the diabetic group (40.8 ± 4.17) as opposed to the group with healthy corneas (37.3 ± 2.89) (p = 0.019, statistically significant). The corneas of the diabetic group showed lower hexagonality (44.36% ± 9.87%) compared with the healthy corneas (59.35% ± 9.67%) (p 0.001, statistically significant). Furthermore, the corneas of the diabetic group had greater central thickness (581.1 ± 32.4 µm) when compared with the control group (511.8 ± 29.8 µm), (p 0.001, statistically significant). No correlation was found between the severity level of diabetic retinopathy and corneal endothelial pathological alterations.Conclusion: Long-term poorly controlled glycaemia has a remarkable impact on corneal endothelium (counts, morphology and structure) as well as corneal thickness.

Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification

Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset.