Early Detection Of Diabetic Retinopathy Research Articles

Hemorrhage Segmentation on Retinal Images for Early Detection of Diabetic Retinopathy

Diabetes mellitus is a chronic disorder that can lead to serious complications, including diabetic retinopathy, which affects the eyes and can potentially lead to blindness. Rapid identification of diabetic retinopathy is crucial to facilitate quicker and more efficient treatment for patients. This study aims to segment hemorrhages in retinal images using the Laplacian of Gaussian (LoG) approach in conjunction with threshold-based segmentation and analysis of region properties, including eccentricity. The LoG approach is utilized for its ability to detect edges, features, and abrupt variations in image intensity, thereby optimally highlighting the bleeding lesion area. With accurate segmentation, it is hoped that early detection and monitoring of diabetic retinopathy can be improved. This research uses the IDRiD, DR_2000, and DIARETDB1 datasets, recommending the use of IDRiD and DIARETDB1 for optimal results. Through this methodology, it is expected to make a significant contribution to reducing the risk of blindness in diabetes patients.

Automated Early Diabetic Retinopathy Detection Using a Deep Hybrid Model

Recently, the primary reason for blindness in adults has been diabetic retinopathy (DR) disease. Therefore, there is an increasing demand for a real-time efficient classification and detection system for diabetic retinopathy (DR) to overcome fast-growing disease (DR). We introduced a novel deep hybrid model for auto-mated diabetic retinopathy (DR) disease recognition and classification. Our model leverages the power of CNN architectures: Inception V3 and VGG16 models by combining their strengths to cater to exact requirements. VGG16 model efficiently captures fine features and wide-ranging features such as textures and edges, crucial for classifying initial signs of DR. Similarly, Inception V3’s architecture is proficient at detecting multiscale patterns, providing an extensive setting for shaping the occurrence of more complex DR severity stages. Our deep hybrid model allows the extraction of various appearance features in retinal images, which can better assist the classification and detection of DR. Our proposed model evaluated on diverse datasets, including EyePACS1 and APTOS2019, demonstrating confident performance of 99.63% accuracy in classifying the DR severity levels on EyePACS1 dataset, while 98.70% accuracy on the APTOS2019 dataset, indicating that our proposed deep hybrid model well distinguished different stages and highly efficient in DR detection. This model helps clinicians and medical experts to classify and identify diabetic retinopathy DR stages and severity levels early. This automatic system helps to manage and treat the patient more effectively and introduces timely treatment.

Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network.

In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness. Early detection of DR and DME is essential to preventing vision loss. Therefore, an enhanced capsule generation adversarial network (ECGAN) optimized with the rat swarm optimization (RSO) approach is proposed in this article to coincide with DR and DME grading (DR-DME-ECGAN-RSO-ISBI 2018 IDRiD). The input images are obtained from the ISBI 2018 unbalanced DR grading data set. Then, the input fundus images are preprocessed using the Savitzky-Golay (SG) filter filtering technique, which reduces noise from the input image. The preprocessed image is fed to the discrete shearlet transform (DST) for feature extraction. The extracting features of DR-DME are given to the ECGAN-RSO algorithm to categorize the grading of DR and DME disorders. The proposed approach is implemented in Python and achieves better accuracy by 7.94%, 36.66%, and 4.88% compared to the existing models, such as the combined DR with DME grading for the cross-disease attention network (DR-DME-CANet-ISBI 2018 IDRiD), category attention block for unbalanced grading of DR (DR-DME-HDLCNN-MGMO-ISBI 2018 IDRiD), combined DR-DME classification with a deep learning-convolutional neural network-based modified gray-wolf optimizer with variable weights (DR-DME-ANN-ISBI 2018 IDRiD).

Revolutionising Diabetic Retinopathy Diagnosis with Modified Regularisation Long Short-Term Memory Framework

The diagnosis of Diabetic Retinopathy (DR) demands a paradigm shift towards more accurate and efficient solutions to overcome vision impairment. Therefore, the current study introduces a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework approach for DR diagnosis. The proposed framework leverages the power of deep learning and provides a dynamic and robust solution for the early detection of DR, which in turn preserves a patient’s vision. The proposed framework uses a DR Debrecen Dataset from the UCI database with 21 distinct features relevant to retinal health, and employs a series of data preprocessing steps, including data cleaning, normalisation, and transformation, to ensure data quality and compatibility. The MR-LSTM framework excels at capturing temporal dependencies in sequential retinal images, offering a unique advantage in understanding the progression of DR. The MR-LSTM framework is implemented using Python libraries, and the results are compared with those of other popular models. It is observed that the MR-LSTM framework outperforms other models and achieves an accuracy of 97.12 percent and an F1 Score of 98.49. Furthermore, the Receiver Operating Characteristic (ROC) curve reveals an area under the curve of 0.97, highlighting the exceptional ability to discriminate between positive and negative cases of the proposed framework. By revolutionising DR diagnosis with the proposed MR-LSTM framework, it can achieve accurate, timely, and accessible solutions in the fight against vision-threatening conditions.

The application of artificial intelligence in diabetic retinopathy: progress and prospects.

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of "presence or absence" to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we're nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

CNN-Based Integrated Framework for Enhanced Diabetic Retinopathy Detection

Background: Diabetic Retinopathy (DR), a significant cause of vision loss globally, is characterized by retinal damage caused by diabetes. Early detection is vital to prevent irreversible blindness, yet challenges remain in accurately identifying DR stages and enhancing blood vessel visibility in fundus images. This paper aims to develop an early detection methodology for DR, addressing the need for early diagnosis and the difficulties in distinguishing DR severity through fundus imaging. The challenges in the early detection of Diabetic Retinopathy (DR) and enhancing blood vessel visibility in fundus images are multifaceted, including issues such as data imbalance, image noise, and complex patterns. By addressing these challenges through advanced ML techniques and image processing methodologies, the proposed methodology in the paper aims to overcome the limitations in early detection and severity assessment of DR, contributing to improved patient outcomes and vision preservation. Methods: This study utilizes Machine Learning (ML) to analyze complex patterns in fundus color images of DR, employing spatial domain filtering to reduce image noise and address data imbalances across DR severity levels through data augmentation. A Convolutional Neural Network (CNN), enhanced with a Gabor filter, is applied for stage-specific DR detection and to pinpoint infected areas. The dataset includes 1000 color fundus images, with a 70:30 split for training and testing, respectively. The adoption of a Gabor filtering technique aims to refine the model’s performance further. Results: The incorporation of a CNN with a Gabor filter has shown outstanding efficacy in detecting DR from fundus color images, achieving a training accuracy of 97.5%, validation accuracy of 96.5%, Cohen kappa score of 89.76%, and testing accuracy of 95.87%. This method effectively illustrates the disease-affected areas in the fundus images provided. Conclusion: Through a comparative analysis of image processing techniques, this research highlights the advantages of using advanced DR analysis for image preprocessing. The proposed CNN-Gabor filter approach demonstrates significant success in identifying diabetic retinopathy in fundus color photographs and accurately delineating the affected regions, contributing valuable insights to the field of medical image processing.

Increasing Diabetic Retinopathy Screening in Resident-Run Clinic Through Partnership With Ophthalmology Clinic: A Pilot Study.

Despite the importance of early detection of diabetic retinopathy, many diabetic patients fail to receive the recommended screening. The objective of this quality-improvement initiative was to increase diabetic retinopathy screening through a partnership between primary care and ophthalmology, where primary care clinic staff may schedule patients directly for screening appointments at point of referral. To our knowledge, this intervention is the first described to use an interspecialty partnership to increase screening. We implemented the intervention at a resident-run primary care clinic with a medically underserved patient population. The pilot intervention took place over a 6-month time frame. The completion rate of diabetic retinopathy screening examinations was compared before and after intervention and was found to increase in a statistically significant manner from 34.7% to 40.5% ( p = .01). The no-show rate did improve from 66.7% preintervention to 46.0% postintervention; however, this change was not statistically significant ( p = .44). During this pilot, the intervention was able to increase diabetic retinopathy screening completion rate; however, further efforts should be aimed at addressing no-shows. Overall, this initiative was a positive step toward the goal of every diabetic patient undergoing the appropriate screening examinations.

Deep learning based binary classification of diabetic retinopathy images using transfer learning approach.

Diabetic retinopathy (DR) is a common problem of diabetes, and it is the cause of blindness worldwide. Detection of diabetic radiology disease in the early detection stage is crucial for preventing vision loss. In this work, a deep learning-based binary classification of DR images has been proposed to classify DR images into healthy and unhealthy. Transfer learning-based 20 pre-trained networks have been fine-tuned using a robust dataset of diabetic radiology images. The combined dataset has been collected from three robust databases of diabetic patients annotated by experienced ophthalmologists indicating healthy or non-healthy diabetic retina images. This work has improved robust models by pre-processing the DR images by applying a denoising algorithm, normalization, and data augmentation. In this work, three rubout datasets of diabetic retinopathy images have been selected, named DRD- EyePACS, IDRiD, and APTOS-2019, for the extensive experiments, and a combined diabetic retinopathy image dataset has been generated for the exhaustive experiments. The datasets have been divided into training, testing, and validation sets, and the models use classification accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC to assess the model's efficiency for evaluating network performance. The present work has selected 20 different pre-trained networks based on three categories: Series, DAG, and lightweight. This study uses pre-processed data augmentation and normalization of data to solve overfitting problems. From the exhaustive experiments, the three best pre-trained have been selected based on the best classification accuracy from each category. It is concluded that the trained model ResNet101 based on the DAG category effectively identifies diabetic retinopathy disease accurately from radiological images from all cases. It is noted that 97.33% accuracy has been achieved using ResNet101 in the category of DAG network. Based on the experiment results, the proposed model ResNet101 helps healthcare professionals detect retina diseases early and provides practical solutions to diabetes patients. It also gives patients and experts a second opinion for early detection of diabetic retinopathy.

Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.

Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. We prospectively recruited adult patients with diabetes at the Centre hospitalier de l'Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.

Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review

Diabetic retinopathy is a disease that can lead to vision loss and blindness in people with diabetes, so its early detection is important to prevent ocular complications. The aim of this study was to analyze the usefulness of artificial intelligence in the detection of diabetic retinopathy. For this purpose, an exploratory systematic review was performed, collecting 77 empirical articles from the Scopus, IEEE, ACM, SciELO and NIH databases. The results indicate that the most commonly used factors for the detection of diabetic retinopathy include changes in retinal vascularization, macular edema and microaneurysms. Among the most commonly applied algorithms for early detection are ResNet 101, CNN and IDx-DR. In addition, some artificial intelligence models are reported to have an accuracy ranging from 90% to 95%, although models with accuracies below 80% have also been identified. It is concluded that artificial intelligence, and in particular deep learning, has been shown to be effective in the early detection of diabetic retinopathy, facilitating timely treatment and improving clinical outcomes. However, ethical and legal concerns arise, such as privacy and security of patient data, liability in case of diagnostic errors, algorithmic bias, informed consent, and transparency in the use of artificial intelligence.

FQPDR: federated quantum neural network for privacy-preserving early detection of diabetic retinopathy

FQPDR: federated quantum neural network for privacy-preserving early detection of diabetic retinopathy

Evaluating the performance of a non-uniform squash function in Capsule networks for early diabetic retinopathy detection using fundus image analysis

Diabetic retinopathy, known as one of the most crucial neurovascular barriers to diabetes, has affected the retina, leading to a potential risk of blindness. The harm is not isolated to personal health, with negative effects felt at the economic and social level in entire communities. The consequences of vision loss secondary to diabetic retinopathy on quality of life, independent living, and employment can be significant. It is necessary to adopt a comprehensive approach for effective management of underlying diabetes, which includes early detection, timely intervention, supportive care, and management of comorbid conditions. Critical to this is a precise classification of its stages for disease analysis. A manual diagnosis, on the other hand, requires qualified individuals to recognize the subtle nuances of the condition, making it a lengthy process. This study aims to develop a computer-assisted system for analyzing retinal fundus images to identify and classify different stages of diabetic retinopathy. A dataset of eye fundus images was used by the proposed network to classify the presence and severity of blindness and its stages. The proposed model uses a non-uniform squash function in capsule neural networks. The motive behind employing a non-uniform squash function was to encourage the model to allocate greater attention and stability to intricate images throughout the training phase. The proposed model's efficiency is assessed meticulously with pre-trained CNN methods like InceptionV3, VGG16, VGG19, and Xception. The proposed capsule network model has been revamped to address the shortcomings of prior research with care. The method stands out with an impressive 99.84 % training accuracy and 98.68 % validation accuracy. Diabetic retinopathy, though rare in occurrence, poses significant challenges due to the high cost of diagnosis, rendering it inaccessible to rural and remote communities with limited access to tests and check-ups.

An efficient explainable deep neural network classifier for diabetic retinopathy detection

Early detection of Diabetic Retinopathy (DR) is crucial for effective intervention, particularly given its often asymptomatic nature in initial stages. Automated detection systems offer significant advantages, such as improving screening efficiency, extending healthcare accessibility to remote regions, and facilitating proactive disease management. Introducing an innovative framework for retinal image analysis that combines explainable deep learning, hybrid feature extraction, and advanced data augmentation to optimize performance and interpretability for clinical applications. The xDNN model, trained on the MESSIDOR-2 dataset, achieved an average recall of 98%, precision of 98.2%, F1-score of 98%, and accuracy of 98.2%. When extensively trained on the APTOS 2019 dataset, the model delivered outstanding results with an average precision, recall, and F1-score of 99%, and an accuracy of 99.7%. Additionally, the model's performance on the IDRID dataset was remarkable, with average precision, recall, F1-score, and accuracy all reaching 99%. Noteworthy is our method impressive average Area Under the Curve (AUC) of 99.8%, affirming its consistent and exceptional performance across all classes of diabetic retinopathy. This underscores the xDNN Classifier's potential as a valuable tool for precise and reliable diabetic retinopathy detection. It holds substantial promise for elevating clinical diagnosis and enhancing treatment outcomes within the field of ophthalmology.

A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification

ABSTRACTDiabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time‐consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR‐2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well‐known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time‐consuming and capable of automatically identifying DR severity levels at a premature level.

Deep Learning for Early Detection of Diabetic Retinopathy: A Colour Fundus Image-based Approach with VGG16 and VGG19

Diabetic retinopathy (DR) is a prevalent complication of diabetes, posing a significant threat to vision. Early detection and intervention are critical to prevent vision loss. However, manual screening of retinal images for DR is time-consuming and susceptible to human error. This study investigates the application of deep learning models, specifically VGG16 and VGG19, for automated DR diagnosis in Colour Fundus images. We evaluate their performance in classifying images as either Diabetic Retinopathy or No Diabetic Retinopathy. Our results demonstrate high testing accuracy with VGG16 achieving 93.96%, suggesting a promising approach for automated DR screening and aiding healthcare professionals in early and accurate diagnosis. Keywords : Convolutional Neural Network, Diabetic Retinopathy, Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19), Transfer Learning, Deep Learning.

Potential Screening, Grading and Follow-Up of Diabetic Retinopathy in Primary Care Using Artificial Intelligence – How Hard Would It Be to Implement? An Ophthalmologist’s Perspective

Diabetic retinopathy (DR) is a microvascular disorder caused by the long-term effects of diabetes mellitus and among the primary causes of blindness worldwide. Early detection of DR is the key to its effective treatment and subsequent reduction of associated economic burden, but manual screening is time-consuming and of limited availability. A highly sensitive and specific automatic diagnostic tool would significantly improve screening programs and allow referring for further evaluation and treatment in an ophthalmology clinic only patients with significant lesions or with changes between two successive evaluations. Several deep learning-based automated diagnosis tools have been proposed to aid screening but their implementation with minimal costs is not accessible to physicians with no coding knowledge. We aimed to develop a fundus images classification model with no coding knowledge by using generative artificial intelligence (AI) implemented in Windows 11 operating system under subscription (Copilot Pro), a free image analysis tool (Fiji ImageJ2), and Vertex AI, a machine learning (ML) platform launched by Google in 2021. For this purpose, we selected a total of 2961 labelled cases from the APTOS 2019 database of DR fundus images. Images were batch segmented using a Java ImageJ script generated by Copilot Pro and based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Segmented images were used to train an automated ML classification model to detect DR severity (5 classes – no DR, mild non-proliferative DR, moderate DR, severe DR, proliferative DR). The model achieved an area under the precision-recall curve of 0.889, with a precision rate of 83.8% and a recall rate of 77%. In conclusion, generative AI implemented into Windows operating system together with a free imaging processing tool and Vertex AI allow ophthalmologists with no coding knowledge to benefit from publicly available image databases (thousands of cases) to develop accurate automated diagnostic tools. Such tools have the potential to facilitate screening especially in areas with few specialists.

An Computational Framework for Enhanced Early Detection of Diabetic Retinopathy with Transfer Learning Methods

Easier early spotting of diabetes retinopathy is important for getting patients help quickly and improving their results. In this research, we used transfer learning techniques to make deep learning models better at automatically finding diabetic retinopathy. Firstly, we used methods for "data augmentation" to make our training sample more diverse. This made our models better at generalization. After this proposed method used fine-tuned the hyperparameters of the transfer learning models. Four transfer learning models caught our attention as MobileNetV2, EfficientNet, VGG16, and a Hybrid Ensemble (DenseNet + Inception). The method focused on the DenseNet model because it had good results in past research and could clearly pick out complex details in medical Images. When combined with data enhancement and fine-tuning, our experiments showed that the DenseNet model worked better than the others in terms of both accuracy and speed. This model, DenseNet, had an amazing success rate of 88.81%, showing that it could be a useful tool for finding diabetic retinopathy early on. The work shows how important transfer learning is in medical image analysis and how to use deep learning models most effectively for finding diabetic retinopathy early. Using advanced machine learning methods to help find and fix diabetic retinopathy will be studied more in the future.

Microaneurysms and Exudates Detection in Retinal Images using Deep Neural Network

One frequent diabetes consequence that affects the eyes is diabetic retinopathy(DR). The most frequent reasonfor blindness in working-age adults in the world is diabetic retinopathy. One in three diabetics has diabetic retinopathy to some extent. DR affects nearly all persons having type 1 diabetes and more than 60% of people having type 2 diabetes to some extent after 20 years of diabetes. In the US, approximately 4.2 million persons, 40 and older have diabetic retinopathy. In the United States, 12% of all new occurrences of blindness are caused by diabetic retinopathy.A 95% reduction in the risk of blindness is possible with a diabetic retinopathy early detection and treatment. The retina's appearance, the presence or absence of Microaneurysm and Exudates, and the degree of participation are all taken into account in the grading process. It has been demonstrated that deep neural networks (DNNs) work well for automatically grading diabetic retinopathy. The features like Microaneurysm and Exudates that are diagnostic of various stages of diabetic retinopathy are taught to these DNNs utilizing vast datasets of retinal pictures and accompanying grading information.It has been demonstrated that deep neural networks (DNNs) are efficient in automatically detecting diabetic retinopathy from retinal pictures. Sensitivity, specificity, precision, accuracy, and Kappa value are used to measure how well DNNs work in detecting diabetic retinopathy; these values are 95.74%, 92.31%, 96.77%, 94.74%, and 0.87, respectively.

Deciphering the impact of diversity in CNN-based ensembles on overcoming data imbalance and scarcity in medical datasets: A case study on diabetic retinopathy

Early detection of diabetic retinopathy (DR) is critical in preventing vision loss. However, building accurate Artificial intelligence (AI) models for multiple classes, including early-stage (Class-1) detection, is challenging due to limited and imbalanced medical datasets. The availability of such datasets is restricted due to ethical and privacy concerns. Traditional ensemble models also struggle with raw medical images, further complicating the issue as they require structured data. This study presents a novel deep learning-based ensemble model (EM) designed for multiple and specifically for precise early stage (Class 1) DR classification. The EM uses eight diverse Convolutional Neural Networks (CNNs) with carefully crafted strategies to enhance diversity. Data augmentation and generation techniques address imbalanced data through data diversity, while parameter and architectural diver-sity within CNNs-based EM maximize predictive performance. Evaluation on the publicly available Kaggle APTOS DR dataset demonstrates significant improvement over individual models and existing approaches. The proposed EM achieves multi-class accuracy (93.00 %), precision (93.00 %), sensitivity (98.00 %), and specificity (99.00 %). This research highlights the effectiveness of diversified CNNs ensembles in overcoming challenges posed by imbalanced and scarce data for multiple-class DR classification. This approach paves the way for developing robust and accurate AI-powered diagnostic tools for improved diabetic retinopathy screening.

Enhanced diabetic retinopathy detection and classification using fundus images with ResNet50 and CLAHE-GAN

Diabetic retinopathy (DR), a progressive eye disorder, can lead to irreversible vision impairment ranging from no DR to severe DR, necessitating precise identification for early treatment. This study introduces an innovative deep learning (DL) approach, surpassing traditional methods in detecting DR stages. It evaluated two scenarios for training DL models on balanced datasets. The first employed image enhancement via contrast limited adaptive histogram equalization (CLAHE) and a generative adversarial network (GAN), while the second did not involve any image enhancement. Tested on the Asia pacific tele-ophthalmology society 2019 blindness detection (APTOS-2019 BD) dataset, the enhanced model (scenario 1) reached 98% accuracy and a 99% Cohen kappa score (CKS), with the non-enhanced model (scenario 2) achieving 95.4% accuracy and a 90.5% CKS. The combination of CLAHE and GAN, termed CLANG, significantly boosted the model's performance and generalizability. This advancement is pivotal for early DR detection and intervention, offering a new pathway to prevent irreversible vision loss in diabetic patients.