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Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases. [ABSTRACT FROM AUTHOR]

Traditional security keys based on passwords are vulnerable to attacks. Recent developments in biometric security systems offer a solution. This study presents a novel approach for generating security keys using the YOLO-v5 deep learning model to detect crossover points in retinal images. We extract RGB hash values from these points and use blockchain to create secure keys. This method enhances security and reduces processing delays. Our approach achieved an F1-score of 69% to 76% on the DRIVE, IOSTAR, and Ibn Al-Haytham datasets, demonstrating promising results compared to existing biometric security technologies. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the leading causes of blindness in a significant portion of the working population, and its damage on vision is irreversible. Therefore, rapid diagnosis on DR is crucial for saving the patient's eyesight. Since Transformer shows superior performance in the field of computer vision compared with Convolutional Neural Networks (CNNs), it has been proposed and applied in computer aided diagnosis of DR. However, a large number of images should be used for training due to the lack of inductive bias in Transformers. It has been demonstrated that the retinal vessels follow self-similar fractal scaling law, and the fractal dimension of DR patients shows an evident difference from that of normal people. Based on this, the fractal dimension is introduced as a prior into Transformers to mitigate the adverse influence of lack of inductive bias on model performance. A new Transformer method pretrained with Masked Autoencoders and fractal dimension (MAEFD) is developed and proposed in this paper. The experiments on the APTOS dataset show that the classification performance for DR by the proposed MAEFD can be substantially improved. Additionally, the present model pretrained with 100,000 retinal images outperforms that pretrained with 1 million natural images in terms of DR classification performance. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a complication of diabetes that damages the retina and can cause blindness if untreated due to high blood sugar levels. To accurately diagnose and grade DR, it is important to identify retinal lacerations or biomarkers. Optical coherence tomography (OCT) imaging is a commonly used tool by ophthalmologists due to its detailed visualisation of retinal lacerations, which aids in the precise treatment of retinal abnormalities. However, the number of scans obtained daily exceeds the ophthalmologist's capacity to meaningfully analyse them, given the wide range of severe OCT applications and the prevalence of visual disorders. In the past, several research studies have attempted to address this issue using OCT scans. However, none of them have tried to simultaneously perform retinal laceration segmentation and DR grading. To address this problem, we have proposed a new architecture—a cutting-edge decoupled convolutional network consisting of three distinct modules that work together to achieve accurate DR grading based on clinical standards aided by retinal laceration segmentation. Our proposed paper introduces a deep learning framework that leverages dual guidance to improve performance on two related tasks. It was extensively tested using 26,841 multi-vendor scans, four publicly available datasets, and a real-time dataset containing 307 OCT scans from various patients. The results confirmed the effectiveness of our design, with a mean Dice score of 0.88 (4.76% improvement) in retinal laceration segmentation and 98.93% accuracy in DR grading, with an actual positive rate of about 98.46% and a true negative rate of 99.37%. [ABSTRACT FROM AUTHOR]

Retinal image registration is crucial for improving the accuracy of diagnosing and monitoring retinal diseases. This paper proposes a specific feature region technique for retinal image registration, involving five steps for accurate registration. The technique combines area and feature-based methods, including extracting the retinal vascular tree, detecting distinctive points, eliminating redundant keypoints, and matching features for image registration using affine transformation modes. First, the retina’s vascular tree is extracted using a Top-Hat operation and optimal thresholding technique. Next, the Harris-PIIFD detector identifies key points in the binary image, and redundant keypoints are removed to reduce computational load. Finally, bilateral matching and best-bin-first algorithms compute the similarity matrix for registering the images. If the image pair is accepted, the points are controlled using the simplest affine transformation modes for the highest registration success rate. The simulation results on 134 pairs of FIRE datasets demonstrate the effectiveness and robustness of the proposed algorithm. The experimental results for the proposed method are satisfactory. The result obtained for the proposed approach for retinal image registration is precision 0.98240, recall 0.98312, RMSE 0.01280, ERR 0.01716, and matching score 0.98284, with the computational time taken 3.01s. This hybrid image registration approach is an efficient and reliable tool for retinal image registration, leading to more accurate diagnosis and monitoring of retinal diseases.

Introduction: The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously. Methods: This study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function. Results and discussion: The experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG. [ABSTRACT FROM AUTHOR]

Fundus cameras are widely used by ophthalmologists for monitoring and diagnosing retinal pathologies. Unfortunately, no optical system is perfect, and the visibility of retinal images can be greatly degraded due to the presence of problematic illumination, intraocular scattering, or blurriness caused by sudden movements. To improve image quality, different retinal image restoration/enhancement techniques have been developed, which play an important role in improving the performance of various clinical and computer-assisted applications. This paper gives a comprehensive review of these restoration/enhancement techniques, discusses their underlying mathematical models, and shows how they may be effectively applied in real-life practice to increase the visual quality of retinal images for potential clinical applications including diagnosis and retinal structure recognition. All three main topics of retinal image restoration/enhancement techniques, i.e., illumination correction, dehazing, and deblurring, are addressed. Finally, some considerations about challenges and the future scope of retinal image restoration/enhancement techniques will be discussed.

Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step. [ABSTRACT FROM AUTHOR]

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time‐consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer‐assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E‐ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively. Research Highlights: The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina.Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment.Currently, manual detection is time‐consuming and requires intense effort.The authors compare qualitative results of the state‐of‐the‐art convolutional neural network (CNN) architectures and propose a computer‐assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters.The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening. [ABSTRACT FROM AUTHOR]

PurposeThe purpose of this study is to identify predictive activation biomarkers in retinal microvascular characteristics of non-exudative macular neovascularization (MNV) and avoid delayed treatment or overtreatment of subclinical MNV. The main objective is to contribute to the international debate on a new understanding of the role of retinal vessel features in the pathogenesis and progression of non-exudative MNV and age-related macular degeneration (AMD). A discussion on revising-related clinical protocols is presented.MethodsIn this retrospective study, the authors included eyes with non-exudative MNV, eyes with exudative AMD, and normal eyes of age-matched healthy subjects. The parameters were obtained by optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA).ResultsIn total, 21 eyes with exudative AMD, 21 eyes with non-exudative MNV, and 20 eyes of 20 age-matched healthy subjects without retinal pathology were included. Vessel density (VD) of the deep vascular complex (DVC) in eyes with non-exudative MNV was significantly greater than that in eyes with exudative AMD (p = 0.002), while for superficial vascular plexus (SVP) metrics, no VD differences among sectors were observed between eyes with non-exudative MNV and eyes with exudative AMD.ConclusionThe reduction in retinal vessel density, especially in the DVC, seems to be involved in or be accompanied by non-exudative MNV activation and should be closely monitored during follow-up visits in order to ensure prompt anti-angiogenic therapy. A discussion on applicable clinical protocols is presented aiming to contribute to new insights into ophthalmology service development which is directed to this specific type of patient and diagnosis.

Age-related macular degeneration (AMD) is a typical fundus disease that affects the central vision of elderly people. It causes difficulties in everyday activities such as reading and recognizing faces. AMD can progress slowly or rapidly, and it leads to severe vision loss if left untreated. Therefore, early detection and diagnosis of AMD are crucial to prevent or delay vision impairment in the elderly. To handle this requirement, researchers are exploring deep learning-based models as an AI tool to assist ophthalmologist in AMD diagnosis. However, conducting an appropriate deep learning model for the AMD classification is challenging and cost-intensive. This research aims to evaluate the efficacy of various deep learning models for obtaining the best performance results when identifying AMD disease using retinal images. To meet this objective, the retinal images from the Department of Ophthalmology, the King Chulalongkorn Memorial Hospital, Thailand were collected for transfer learning and other publicly available datasets for testing. Then, seven deep learning models VGG19, Xception, DenseNet201, EfficientNetB7, InceptionV3, NASNetLarge, and ResNet152V2 were chosen to training for the 2-labels (Normal vs. AMD) and the 3-labels (Normal vs. Dry AMD vs. Wet AMD) classifications. From the experimental results, the DenseNet201 model with Dense block in its structure showed the best efficacy in both 2-labels and 3-labels AMD classifications since its performance always include in the Top-3 models accuracy and generalization performance measured by total accuracy and total F1-Score, respectively. Furthermore, the accuracy performance of deep learning models in Top-3 are comparable with the performance of retinal specialist. These results contribute consolidated knowledge to the process of implementation effective deep learning as production that detects AMD automatically in the clinical and enhance the quality of healthcare service.

Diabetes is a common chronic disease and a major public health problem approaching epidemic proportions globally. People with diabetes are more likely to suffer from glaucoma than people without diabetes. Glaucoma can lead to loss of vision if not diagnosed at an early stage. This study proposes an intelligent computer-aided triage system with a deep neural network and machine learning to develop and analyze color retinal fundus images and classify glaucomatous retinal images. Deep features of retinal images from the fundus retinal image are extracted using a deep neural network, and the classification of features is performed and analyzed using different machine learning classifiers. Experimental results show that the combination of deep neural network and logistic regression-based classifier outperforms all existing glaucomatous triage systems, improving classification accuracy, sensitivity, and specificity.

By incorporating a signature into multimedia files, notably pictures, watermarking is a technique for preventing destructive attacks. Inversely, traditional watermarking techniques have limitations when it comes to sensitive images like those used in the medical field. In fact, the additional mark frequently alters the image permanently and may hide subtle characteristics. In this study, we provide a watermarking method for patient identification and watermark integrity verification to address these problems. In this procedure, the patient's encrypted photo is included in the second section of the watermark, which also carries the patient's information fingerprint. In this method, the frequency content of the image is extracted using a redundant discrete wavelet transform. Then, the coefficients are subjected to Schur decomposition, and the watermark bits are integrated by altering the Eigen values' least significant bit. This method's capacity enables the integration of the patient's fingerprint and photo as well as the addition of an error-correcting code. The watermark could be nearly totally concealed due to the coefficient modulation of the integration process. As a result, we were able to produce a watermarked image that was similar to the original in terms of the structure. The effectiveness of the approach as well as its resilience to JPEG compression and noise addition were assessed through experiments on test images. However, these outcomes occasionally deviate from the ideal value of 1 for normalized correlation. [ABSTRACT FROM AUTHOR]

Background and Objectives: Glaucoma is a major cause of irreversible visual impairment and blindness, so its timely detection is crucial. Retinal images from diabetic retinopathy screening programmes (DRSP) provide an opportunity to detect undiagnosed glaucoma. Our aim was to find out which retinal image indicators are most suitable for referring DRSP patients for glaucoma assessment and to determine the glaucoma detection potential of Slovenian DRSP. Materials and Methods: We reviewed retinal images of patients from the DRSP at the University Medical Centre Ljubljana (November 2019–January 2020, May–August 2020). Patients with at least one indicator and some randomly selected patients without indicators were invited for an eye examination. Suspect glaucoma and glaucoma patients were considered accurately referred. Logistic regression (LOGIT) with patients as statistical units and generalised estimating equation with logistic regression (GEE) with eyes as statistical units were used to determine the referral accuracy of indicators. Results: Of the 2230 patients reviewed, 209 patients (10.1%) had at least one indicator on a retinal image of either one eye or both eyes. A total of 149 (129 with at least one indicator and 20 without) attended the eye exam. Seventy-nine (53.0%) were glaucoma negative, 54 (36.2%) suspect glaucoma, and 16 (10.7%) glaucoma positive. Seven glaucoma patients were newly detected. Neuroretinal rim notch predicted glaucoma in all cases. The cup-to-disc ratio was the most important indicator for accurate referral (odds ratio 7.59 (95% CI 3.98–14.47; p < 0.001) and remained statistically significant multivariably. Family history of glaucoma also showed an impact (odds ratio 3.06 (95% CI 1.02–9.19; p = 0.046) but remained statistically significant only in the LOGIT multivariable model. Other indicators and confounders were not statistically significant in the multivariable models. Conclusions: Our results suggest that the neuroretinal rim notch and cup-to-disc ratio are the most important for accurate glaucoma referral from retinal images in DRSP. Approximately half of the glaucoma cases in DRSPs may be undiagnosed. [ABSTRACT FROM AUTHOR]

Eye diseases that are common and many diseases that result in visual ailments, such as diabetes and vascular disease, can be diagnosed through retinal imaging. The enhancement of retinal images often helps in diagnosing diseases related to retinal organ failure. However, today's image enhancement methods may lead to artificial boundaries, sudden color gradation, and the loss of image details. Therefore, to prevent these side effects, a new method of retinal image enhancement is proposed. In this work, we propose a new method for enhancing the overall contrast of colored retinal images. That is, we propose low-light image enhancement using a new retinex method based on a powerful semidecoupled retinex method. In particular, illumination layer I gradually approximates the S input image according to the file. This leads to a complete Gaussian transformation model, while the R-layer reflectance is estimated jointly by S and intermediary by I to suppress image noise simultaneously during R estimation on the publicly available Messidor database. From our assessment measurements (PSNR and SSIM), we show that this proposed method is effective in comparison with the relevant and recently proposed retinal imaging methods; moreover, the color, which is determined by the data, does not change the image structure. Finally, a technique is presented to improve the pronounced color of a retinal image, which is useful for ophthalmologists to screen for retinal disease more effectively. Moreover, this technique can be used in the development of robotics for imaging tests to search for clinical markers. [ABSTRACT FROM AUTHOR]

Retinal imaging can be used to identify a variety of common eye and cardiac disorders. However, owing to non-uniform or poor illumination and low contrast, low-quality retinal fundus medical images are ineffective for diagnostic, particularly in computerized image analysis systems. The article proposes an effective image enhancement method for improving the luminosity and contrast of color retinal fundus images. To begin, the input color retinal fundus image is transformed to HSV (Hue, Saturation, and Value) color model, which separates the luminance channel (V) from the other color elements hue (H) and saturation (S). Then, on the luminosity channel (V), a new JND-based adaptive gamma correction method is utilized to improve the luminance of fundus images. After that, contrast is improved in the luminance component in the L*a*b* color space, employing a novel contrast enhancement technique that employs several layers of CLAHE (contrast limited adaptive histogram equalization). These two techniques substantially improve the overall luminance and contrast in retinal images while preserving the average brightness, keeping an original appearance, and maximizing the entropy of the input retinal fundus images. Experiments on a broad range of fundus images are performed to assess the proposed scheme's performance both qualitatively and quantitatively. Substantial objective evaluation indicates that the proposed scheme surpasses state-of-the-art enhancement techniques in terms of edge preservation index, entropy, a measure of enhancement, contrast ratio, and enhancement metrics. This retinal fundus image enhancement method can be employed to support ophthalmologists in effectively inspecting for retinal disorders and developing more accurate computerized image analysis for medical diagnosis. [ABSTRACT FROM AUTHOR]

Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step.

Retinal images play a crucial role in the clinical diagnosis and detection of many eye diseases. However, the retinal image is degraded with noise and suffers from low contrast, which makes it difficult to interpret the image precisely and accurately. So retinal image enhancement becomes a vital step before any further processing. Here, a new approach is suggested for fundus image enhancement, where the image is processed in the spatial domain through an optimal statistical feature‐based transformation function (SFTF). The proposed SFTF has four tunable parameters. An optimal set of these parameters is obtained through a newly suggested adaptive enhanced leader particle swarm optimization technique. The suggested method is investigated with freely accessible DRIVE, STARE, and IDRiD databases. The efficiency of the suggested technique is assessed through different validation indices. The results reveal superior performance compared with other popular methods used for vessel enhancement. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate vessel segmentation for retinal image is the most conducive to early diagnosis of various eye-related diseases. Most deep learning models are used to segment the vessel by green channel grey image and random sampling of whole image that lead to insufficient training samples and low accuracy probably. To address this issue, a novel retinal vessel segmentation model based on deep learning is proposed, which is called Multichannel U-Net (M-UNet) combined with multi-scale equalization sampling for extracting vessel networks. The multi-scale equalization sampling patches are used to splice an image as new data. And the M-UNet structure is utilized to train the retinal vessel segmentation model. The input of the model is the three channel image replacing the grey image. The DRIVE and STARE database are used to evaluate the performance. Experimental results indicate that the proposed method can obtain better vessel networks, especially for the extraction of peripheral vascular structure. The average accuracy of DRIVE is 0.9716, sensitivity is 0.8168, specificity is 0.9860, and Area Under Curve (AUC) is up to 0.9843. The performances are more competitive than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Additional layers to the U-Net architecture leads to additional parameters and network complexity. The Visual Geometry Group (VGG) architecture with 16 backbones can overcome the problem with small convolutions. Dense Connected (DenseNet) can be used to avoid excessive feature learning in VGG by directly connecting each layer using input from the previous feature map. Adding a Dropout layer can protect DenseNet from Overfitting problems. This study proposes a VG-DropDNet architecture that combines VGG, DenseNet, and U-Net with a dropout layer in blood vessels retinal segmentation. VG-DropDNet is applied to Digital Retina Image for Vessel Extraction (DRIVE) and Retina Structured Analysis (STARE) datasets. The results on DRIVE give great accuracy of 95.36%, sensitivity of 79.74% and specificity of 97.61%. The F1-score on DRIVE of 0.8144 indicates that VG-DropDNet has great precision and recall. The IoU result is 68.70. It concludes that the resulting image of VG-DropDNet has a great resemblance to its ground truth. The results on STARE are excellent for accuracy of 98.56%, sensitivity of 91.24%, specificity of 92.99% and IoU of 86.90%. The results of the VGG-DropDNet on STARE show that the proposed method is excellent and robust for blood vessels retinal segmentation. The Cohen’s Kappa coefficient obtained by VG-DropDNet at DRIVe is 0.8386 and at STARE is 0.98, it explains that the VG-DropDNet results are consistent and precise in both datasets. The results on various datasets indicate that VG-DropDnet is effective, robust and stable in retinal image blood vessel segmentation.

Background: Diabetic retinopathy (DR) is an autoimmune disorder that affects the human eyes, causing lesions on the retina as a consequence of diabetes mellitus. Early identification of DR is crucial for effective vision maintenance and preventing severe vision loss.To develop and implement an automated and novel approach for the detection and classification of diabetic retinopathy, addressing the limitations of conventional DR detection systems which include complex disease detection, time-consuming processes, and low training efficiency.The proposed work introduces an Improved Intelligent Water Drop (I2WD) optimization algorithm for selecting the most correlated features from the extracted feature set, thereby reducing the complexity of the classifier. For the prediction of DR, the Discriminated Multi-Instance Classification (DMIC) algorithm is employed, known for its higher accuracy and lower rate of incorrect predictions.The proposed Item Net–based I2WD-DMIC model is tested, validated, and compared using well-known benchmark datasets. The results demonstrate significant improvements in accuracy and efficiency over conventional DR detection methods.The novel I2WD-DMIC approach offers a robust and efficient solution for diabetic retinopathy detection and classification, overcoming the typical limitations of traditional systems. This method shows promise in enhancing early diagnosis and improving patient outcomes in clinical settings.Objective: Diabetic retinopathy (DR) is an autoimmune disorder that affects the human eyes, causing lesions on the retina as a consequence of diabetes mellitus. Early identification of DR is crucial for effective vision maintenance and preventing severe vision loss.To develop and implement an automated and novel approach for the detection and classification of diabetic retinopathy, addressing the limitations of conventional DR detection systems which include complex disease detection, time-consuming processes, and low training efficiency.The proposed work introduces an Improved Intelligent Water Drop (I2WD) optimization algorithm for selecting the most correlated features from the extracted feature set, thereby reducing the complexity of the classifier. For the prediction of DR, the Discriminated Multi-Instance Classification (DMIC) algorithm is employed, known for its higher accuracy and lower rate of incorrect predictions.The proposed Item Net–based I2WD-DMIC model is tested, validated, and compared using well-known benchmark datasets. The results demonstrate significant improvements in accuracy and efficiency over conventional DR detection methods.The novel I2WD-DMIC approach offers a robust and efficient solution for diabetic retinopathy detection and classification, overcoming the typical limitations of traditional systems. This method shows promise in enhancing early diagnosis and improving patient outcomes in clinical settings.Methods: Diabetic retinopathy (DR) is an autoimmune disorder that affects the human eyes, causing lesions on the retina as a consequence of diabetes mellitus. Early identification of DR is crucial for effective vision maintenance and preventing severe vision loss.To develop and implement an automated and novel approach for the detection and classification of diabetic retinopathy, addressing the limitations of conventional DR detection systems which include complex disease detection, time-consuming processes, and low training efficiency.The proposed work introduces an Improved Intelligent Water Drop (I2WD) optimization algorithm for selecting the most correlated features from the extracted feature set, thereby reducing the complexity of the classifier. For the prediction of DR, the Discriminated Multi-Instance Classification (DMIC) algorithm is employed, known for its higher accuracy and lower rate of incorrect predictions.The proposed Item Net–based I2WD-DMIC model is tested, validated, and compared using well-known benchmark datasets. The results demonstrate significant improvements in accuracy and efficiency over conventional DR detection methods.The novel I2WD-DMIC approach offers a robust and efficient solution for diabetic retinopathy detection and classification, overcoming the typical limitations of traditional systems. This method shows promise in enhancing early diagnosis and improving patient outcomes in clinical settings.Results: Diabetic retinopathy (DR) is an autoimmune disorder that affects the human eyes, causing lesions on the retina as a consequence of diabetes mellitus. Early identification of DR is crucial for effective vision maintenance and preventing severe vision loss.To develop and implement an automated and novel approach for the detection and classification of diabetic retinopathy, addressing the limitations of conventional DR detection systems which include complex disease detection, time-consuming processes, and low training efficiency.The proposed work introduces an Improved Intelligent Water Drop (I2WD) optimization algorithm for selecting the most correlated features from the extracted feature set, thereby reducing the complexity of the classifier. For the prediction of DR, the Discriminated Multi-Instance Classification (DMIC) algorithm is employed, known for its higher accuracy and lower rate of incorrect predictions.The proposed Item Net–based I2WD-DMIC model is tested, validated, and compared using well-known benchmark datasets. The results demonstrate significant improvements in accuracy and efficiency over conventional DR detection methods.The novel I2WD-DMIC approach offers a robust and efficient solution for diabetic retinopathy detection and classification, overcoming the typical limitations of traditional systems. This method shows promise in enhancing early diagnosis and improving patient outcomes in clinical settings.Conclusion: Diabetic retinopathy (DR) is an autoimmune disorder that affects the human eyes, causing lesions on the retina as a consequence of diabetes mellitus. Early identification of DR is crucial for effective vision maintenance and preventing severe vision loss.To develop and implement an automated and novel approach for the detection and classification of diabetic retinopathy, addressing the limitations of conventional DR detection systems which include complex disease detection, time-consuming processes, and low training efficiency.The proposed work introduces an Improved Intelligent Water Drop (I2WD) optimization algorithm for selecting the most correlated features from the extracted feature set, thereby reducing the complexity of the classifier. For the prediction of DR, the Discriminated Multi-Instance Classification (DMIC) algorithm is employed, known for its higher accuracy and lower rate of incorrect predictions.The proposed Item Net–based I2WD-DMIC model is tested, validated, and compared using well-known benchmark datasets. The results demonstrate significant improvements in accuracy and efficiency over conventional DR detection methods.The novel I2WD-DMIC approach offers a robust and efficient solution for diabetic retinopathy detection and classification, overcoming the typical limitations of traditional systems. This method shows promise in enhancing early diagnosis and improving patient outcomes in clinical settings. [ABSTRACT FROM AUTHOR]

Background and Objectives: Glaucoma is a major cause of irreversible visual impairment and blindness, so its timely detection is crucial. Retinal images from diabetic retinopathy screening programmes (DRSP) provide an opportunity to detect undiagnosed glaucoma. Our aim was to find out which retinal image indicators are most suitable for referring DRSP patients for glaucoma assessment and to determine the glaucoma detection potential of Slovenian DRSP. Materials and Methods: We reviewed retinal images of patients from the DRSP at the University Medical Centre Ljubljana (November 2019–January 2020, May–August 2020). Patients with at least one indicator and some randomly selected patients without indicators were invited for an eye examination. Suspect glaucoma and glaucoma patients were considered accurately referred. Logistic regression (LOGIT) with patients as statistical units and generalised estimating equation with logistic regression (GEE) with eyes as statistical units were used to determine the referral accuracy of indicators. Results: Of the 2230 patients reviewed, 209 patients (10.1%) had at least one indicator on a retinal image of either one eye or both eyes. A total of 149 (129 with at least one indicator and 20 without) attended the eye exam. Seventy-nine (53.0%) were glaucoma negative, 54 (36.2%) suspect glaucoma, and 16 (10.7%) glaucoma positive. Seven glaucoma patients were newly detected. Neuroretinal rim notch predicted glaucoma in all cases. The cup-to-disc ratio was the most important indicator for accurate referral (odds ratio 7.59 (95% CI 3.98–14.47; p < 0.001) and remained statistically significant multivariably. Family history of glaucoma also showed an impact (odds ratio 3.06 (95% CI 1.02–9.19; p = 0.046) but remained statistically significant only in the LOGIT multivariable model. Other indicators and confounders were not statistically significant in the multivariable models. Conclusions: Our results suggest that the neuroretinal rim notch and cup-to-disc ratio are the most important for accurate glaucoma referral from retinal images in DRSP. Approximately half of the glaucoma cases in DRSPs may be undiagnosed.

Diabetic retinopathy (DR) is a late microvascular complication of Diabetes Mellitus (DM) that could lead to permanent blindness in patients, without early detection. Although adequate management of DM via regular eye examination can preserve vision in in 98% of the DR cases, DR screening and diagnoses based on clinical lesion features devised by expert clinicians; are costly, time-consuming and not sufficiently accurate. This raises the requirements for Artificial Intelligent (AI) systems which can accurately detect DR automatically and thus preventing DR before affecting vision. Hence, such systems can help clinician experts in certain cases and aid ophthalmologists in rapid diagnoses. To address such requirements, several approaches have been proposed in the literature that use Machine Learning (ML) and Deep Learning (DL) techniques to develop such systems. However, these approaches ignore the highly valuable clinical lesion features that could contribute significantly to the accurate detection of DR. Therefore, in this study we introduce a framework called DR-detector that employs the Extreme Gradient Boosting (XGBoost) ML model trained via the combination of the features extracted by the pretrained convolutional neural networks commonly known as transfer learning (TL) models and the clinical retinal lesion features for accurate detection of DR. The retinal lesion features are extracted via image segmentation technique using the UNET DL model and captures exudates (EXs), microaneurysms (MAs), and hemorrhages (HEMs) that are relevant lesions for DR detection. The feature combination approach implemented in DR-detector has been applied to two common TL models in the literature namely VGG-16 and ResNet-50. We trained the DR-detector model using a training dataset comprising of 1,840 color fundus images collected from e-ophtha, retinal lesions and APTOS 2019 Kaggle datasets of which 920 images are healthy. To validate the DR-detector model, we test the model on external dataset that consists of 81 healthy images collected from High-Resolution Fundus (HRF) dataset and MESSIDOR-2 datasets and 81 images with DR signs collected from Indian Diabetic Retinopathy Image Dataset (IDRID) dataset annotated for DR by expert. The experimental results show that the DR-detector model achieves a testing accuracy of 100% in detecting DR after training it with the combination of ResNet-50 and lesion features and 99.38% accuracy after training it with the combination of VGG-16 and lesion features. More importantly, the results also show a higher contribution of specific lesion features toward the performance of the DR-detector model. For instance, using only the hemorrhages feature to train the model, our model achieves an accuracy of 99.38 in detecting DR, which is higher than the accuracy when training the model with the combination of all lesion features (89%) and equal to the accuracy when training the model with the combination of all lesions and VGG-16 features together. This highlights the possibility of using only the clinical features, such as lesions that are clinically interpretable, to build the next generation of robust artificial intelligence (AI) systems with great clinical interpretability for DR detection. The code of the DR-detector framework is available on GitHub at https://github.com/Janga-Lab/DR-detector and can be readily employed for detecting DR from retinal image datasets.

Objective: Due to limited imaging conditions, the quality of fundus images is often unsatisfactory, especially for images photographed by handheld fundus cameras. Here, we have developed an automated method based on combining two mirror-symmetric generative adversarial networks (GANs) for image enhancement. Methods: A total of 1047 retinal images were included. The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment. All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists. The quality classification and quality change of images were compared. In addition, image-detailed reading results for the number of dubiously pathological fundi were also compared. Results: After GAN enhancement, 42.9% of images increased their quality, 37.5% remained stable, and 19.6% decreased. After excluding the images at the highest level (level 0) before enhancement, a large number (75.6%) of images showed an increase in quality classification, and only a minority (9.3%) showed a decrease. The GAN-enhanced method was superior for quality improvement over a luminosity and contrast adjustment method (P＜0.001). In terms of image reading results, the consistency rate fluctuated from 86.6% to 95.6%, and for the specific disease subtypes, both discrepancy number and discrepancy rate were less than 15 and 15%, for two ophthalmologists. Conclusions: Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.

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