Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity

There are countless number of people with diabetes around the world. diabetic retinopathy (DR), a major complication of diabetes, is a retinal disease that results in blindness if not treated in the earlier stages. Early detection of diabetic retinopathy in patients can help avoid further loss of eyesight. For early detection, it is important for diabetic patients to undergo medical tests after a frequent amount of times. Diagnosing DR using fundus images is a difficult and time-consuming task which requires professional expertise to find and identify if the DR traits are present in the eye. In our proposed system, we make use of artificial intelligence, convolutional neural networks precisely to make the process of detection and classification of diabetic retinopathy a lot simpler and fast. The proposed model makes use of AlexNet which is a convolutional neural network architecture which is trained based on fundus image database to accurately diagnose DR with minimum efforts. The database contains multiple cases of retinal hemorrhage, microaneurism, cotton wool spots, etc. The retinal image of the patient is fed to the system where initially the image is trimmed to match the input layer of AlexNet and then the neural network extracts features and the model gets trained in order to further classify the diabetic retinopathy patients. The last layer of the convolutional neural network is developed in such a way each segment of image is assigned a specific weight factor where higher the weight, more is the chances of getting severe DR.

Identification and classification of microaneurysms for early detection of diabetic retinopathy

In the modern world, diabetes is one of the major health problems that affects humans all around the world. Lack of early detection, prolonged diabetics, might lead to medical complications such as heart problems, eye vision problems, skin issues, etc. Diabetic retinopathy (DR) is a frequent abnormality of diabetics. To help patients with the early detection of diabetic retinopathy, in this paper, we propose a computer vision-based technique to analyze and predict diabetes from the retinal input images. Image preprocessing, segmentation, and feature extraction steps are applied. Convolutional neural networks (CNN) and Support Vector Machine (SVM) are trained with diabetic and non-diabetic retinal images. Results show CNN reports better accuracy in DR compared to SVM.

Diabetic retinopathy, a condition resulting from prolonged high blood sugar levels that damage the retina, can cause vision impairment and, if untreated, lead to blindness. With advances in medical imaging and the availability of fundus image collections such as Madrid Messidor and DRIVE, computer-aided diagnosis (CAD) systems have become instrumental in identifying and categorizing cases. Machine learning, a branch of artificial intelligence, has demonstrated remarkable success in medical image processing, showing great potential for the early detection of diabetic retinopathy—a condition often challenging to diagnose in its early stages due to a lack of symptoms. This review examines prior studies leveraging machine learning algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest neighbors (KNN), for diabetic retinopathy detection using fundus image datasets. It also explores existing challenges, including dataset variability, computational demands, and the generalizability of models across diverse populations. Highlighting methodologies, datasets, and performance metrics like accuracy, sensitivity, and specificity, this article aims to provide a cohesive understanding of the current landscape, delineate strengths and limitations, and suggest directions for future research.

This paper explores the application of deep learning architectures in retinal image analysis, with a particular focus on the early detection of diabetic retinopathy (DR). Leveraging large-scale retinal imaging datasets, the study examines the diagnostic capacity of advanced neural models in identifying subtle pathological markers such as microaneurysms, hemorrhages, and exudates at the earliest clinical stages. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid deep learning frameworks are evaluated for their performance in image classification and lesion localization. Results demonstrate that CNN-based architectures achieve an accuracy of 94.2%, with sensitivity and specificity values exceeding 93%, underscoring their robustness in medical image interpretation. Hybrid models integrating CNN and attention mechanisms further enhance feature discrimination, achieving 95.1% accuracy and improved area under the curve (AUC) metrics. RNN-driven architectures exhibit competitive performance, particularly in sequential image feature learning, with overall accuracy surpassing 92%. The findings highlight the transformative role of deep learning in automating ophthalmic diagnostics, enabling scalable, precise, and timely detection of diabetic retinopathy to mitigate preventable vision loss.

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, affecting millions of dia- betic patients. Early detection and accurate classification of DR stages are crucial for preventing vision loss and improving patient outcomes. This survey paper presents a comprehensive anal- ysis of deep learning approaches for automatic detection and classification of diabetic retinopa- thy from fundus images. The proposed system utilizes Faster Region-based Convolutional Neural Networks (Faster R-CNN) architecture to accurately identify and categorize the sever- ity of DR in retinal images. The methodology encompasses image acquisition using fundus cameras, preprocessing for quality enhancement, deep feature learning for data-driven char- acterizations, and classification into various DR stages including No DR, Mild DR, Moderate DR, Proliferative DR, and Severe DR. This automated approach offers significant advantages over traditional manual screening methods, providing faster, more consistent, and cost-effective diagnosis capabilities. The technical implementation focuses on developing robust algorithms for fundus image analysis, including preprocessing techniques for image enhancement, feature extraction using convolutional neural networks, and classification models optimized for medical imaging appli- cations. The system addresses challenges such as image quality variations, lighting conditions, and anatomical differences across patients. Performance evaluation encompasses accuracy met- rics, sensitivity, specificity, and area under the ROC curve (AUC) to ensure clinical reliability. The proposed Faster R-CNN architecture demonstrates superior performance compared to tra- ditional methods, achieving high accuracy in DR detection while maintaining computational efficiency suitable for clinical deployment. This automated screening system has the potential to significantly improve early detection rates, reduce healthcare costs, and enhance patient care quality in diabetic retinopathy management.

Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning

This article demonstrates a revolutionary deep- learning approach for automated diabetic retinopathy (DR) detection using convolutional neural networks (CNNs). With the help of datasets from the Diabetic Retinopathy Detection 2015 and APTOS 2019 Blindness Detection competitions, our model attempts to accurately classify retinal images into two categories: No DR and Proliferative DR, based on the severity of macular degeneration. We enhance the quality of the dataset and the robustness of the model by carefully preprocessing and augmenting it using various approaches like rotation, flipping, shearing, rescaling, translation, brightness correction, and normalization. We train two models: a regression model and a deep learning model. With an 88% validation accuracy, we demonstrated that our deep learning technique performs better than the regression model. In order to properly determine and classify DR severity levels, this study presents a robust and noise-tolerant system that evolves the field of automated DR detection. Our approach enables early detection and intervention by combining cutting-edge CNN architectures with sophisticated preprocessing techniques, which may lessen the strain of DR- related blindness on healthcare systems. We also demonstrate out drawbacks and suggest areas for additional investigation, such as analyzing ensemble learning and using a variety of datasets to improve the generalizability of the model. As we wrap up, our study emphasizes the potential of deep learning techniques to transform DR diagnosis and treatment, with the goal of enhancing the quality of care for diabetics globally.

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.

Diabetes is one of the most dangerous diseases that is facing by many people in the world. Diabetic Retinopathy (DR) is a retinal disease that is caused by the Diabetes Mellitus (DM). DM causes the lesions on the retinal layer thereby affecting the vision. If it is not detected at the initial stages, it might lead to complete blindness and also DR is an irreversible disease. Hence, early detection of DR is inevitable to avoid the vision loss of the patients. Expeditious detection can decrease the complications of the DR thereby vision of the patient is preserved. It is a laborious task for the ophthalmologists to diagnosis DR manually, since it takes a lot more time and cost-effective. Mis-diagnosis might happen if the ophthalmologists are not skillful and experienced in detecting DR from the fundus images. Over the past two decades, deep learning has shown a significant raise in the bio-medical image processing and its niche areas obtaining the best performance. The state-of-the-art Convolutional Neural Networks (CNN) models achieved notable performance in classifying DR, but the severity levels of DR are not analyzed. To address these challenges, we propose a CNN based model that is used to analyze the fundus oculi retinal images to locate the eyeball structure and observe the presence of DR. The proposed model's hyperparameters are regulated by the transfer learning techniques for mapping the label of the images. The dataset used for training and testing the model is taken from the Kaggle that contains the retinal images and its corresponding severity in a scale. Severity level of the images is classified into five different categories from a normal to DR presented eyeball. The accuracy of the proposed model is 94.92%, proving the confident detection and prediction of the DR from the retinal images.

Background Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with rising prevalence particularly in low-resource settings. Traditional DR screening relies on specialist ophthalmologic assessment, posing barriers to early detection. Recent advances in deep learning have enabled the development of automated tools that may enhance the scalability and accessibility of DR screening. Objectives To compare the performance of two convolutional neural network (CNN) architectures-EfficientNet-B5 and ResNet-50-for automated DR detection using publicly available retinal fundus image datasets. Methods We utilized a transfer learning approach on two large datasets: EyePACS 2015 (n = 35,126) for training/validation and APTOS 2019 (n = 3,662) for external testing. Images were preprocessed with resizing, cropping, and contrast enhancement. The EfficientNet-B5 and ResNet-50 models were independently trained and evaluated using key metrics: accuracy, sensitivity, specificity, area under the ROC curve (AUC), and Quadratic Weighted Kappa (QWK), with classification across DR severity levels. Results EfficientNet-B5 achieved a QWK of 92%, sensitivity of 98%, specificity of 93%, and AUC of 96% on the test dataset. ResNet-50 demonstrated similarly high performance with an accuracy of 96%, QWK of 86%, sensitivity of 94%, specificity of 97%, and AUC of 96%. Both models showed robust generalization across severity grades, indicating strong discriminative capabilities. Conclusions The results highlighted that our models are viable architectures for AI-driven DR screening, with EfficientNet-B5 showing slightly higher agreement scores and ResNet-50 offering stronger specificity. These models hold promise for deployment in DR screening programs, especially in resource-limited settings where ophthalmologic expertise is scarce. Future work should focus on clinical validation and integration into public health systems to improve early DR detection and reduce vision-related morbidity. Key messages • Integrating artificial intelligence (AI) into diabetic retinopathy screening can enhance early detection and support equitable access to eye care, particularly in resource-limited settings. • AI-based screening tools offer a scalable and effective approach to strengthen public health strategies aimed at preventing vision loss from diabetic complications.

Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.

Diabetic retinopathy (DR) is a leading cause of diabetes mellitus, which seeds lesions on the retina that helps in sequel vision. If it is not identified in the beginning, it will lead to blindness. Unfortunately, DR is an unalterable process and treatment only assists vision. The risk of vision loss has a remarkable reduction with early detection and treatment. The process of manual detection of fundus DR images by ophthalmologists is time-consuming, cost-intensive, and vulnerable to misdiagnosis rather than computer-aided diagnosis methods. Lately, deep learning is one of the most familiar techniques that has attained finer performance in numerous fields, mainly in medical image scanning and categorization. The objective of this survey paper is to provide a review of various methods applied to diabetic retinopathy. A comparative study is made and the highest accuracy achieved is 92.5% for the detection of DR using a convolutional neural network (CNN). In this survey paper, the recent state-of-procedures for diabetic retinopathy color fundus image identification and categorization using DL strategies have been evaluated and analyzed. In addition, the DR datasets for the color fundus images of the retina have been revised. Divergent challenging issues that need detailed examination are also analyzed.KeywordsDeep learning (DL)Diabetic retinopathy (DR)Convolutional neuralNetwork (CNN)LesionImage processingDiabetic macular edema

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.

Artificial intelligence for diabetic retinopathy screening in Africa