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Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices. [ABSTRACT FROM AUTHOR]

Abstract Early detection and correct identification of the optic disc (OD) on scanned retinal images are significant for diagnosing and treating several ophthalmic conditions, including glaucoma and diabetic retinopathy. Conventional methods for detecting the OD often struggle with processing retinal images due to noise, changes in illumination, and complex overlapping images. This study presents the development of effective and accurate fixation of the optic disc using the Bitterling Fish Optimization (BFO) algorithm, which enhances the processes of OD imaging in speed and precision. The proposed method begins with image enhancement and noise suppression for preprocessing, followed by applying the BFO algorithm to locate and delineate the OD region. The performance evaluation of the algorithm was conducted within several public domain retinal images, including DRIVE, STARE, ORIGA, DRISHTI-GS, DiaRetDB0, and DiaRetDB1 datasets about some internal metrics: sensitivity (SE), specificity (SP), accuracy (ACC), DICE overlap coefficient, overlap and time of processing respectively. The technique based on BFO provided better results, with 99.33%, 99.94%, and 98.22% accuracy achieved for OD in DRIVE, DRISHTI-GS, and DiaRetDB 1, respectively. The approach also demonstrated high overlaps and good DICE results, with a DICE coefficient of 0.9501 for the DRISHTI-GS database. On average, the processing time per image was less than 2.5 s, proving the approach’s efficiency in computations. The BFO approach has demonstrated its effectiveness and scalability in detecting optic discs in retinal images in an automated manner. It showed impressive performance levels in terms of computation time and accuracy and was variation resistant irrespective of the quality of the image and the pathology present on it. This method holds significant potential for clinical use, providing a meaningful way of diagnosing and managing ocular disease at an early stage.

One of the common eye conditions affecting patients with diabetes is diabetic retinopathy (DR). It is characterised by the progressive impairment to the blood vessels with the increase of glucose level in the blood. The grading efficiency still finds challenging because of the existence of intra-class variations and imbalanced data distributions on the retinal images. Traditional machine learning techniques utilise hand-engineered features for classification of the affected retinal images. As convolutional neural network produces better image classification accuracy in many medical images, this work utilises the CNN-based feature extraction method. This feature has been used to build Gaussian mixture model (GMM) for each class that maps the CNN features to log-likelihood dimensional vector spaces. Since the Gaussian mixture model can be realised as a mixture of both parametric and nonparametric density models and has their flexibility in capturing different data distributions, probabilistic outputs, interpretability, efficient parameter estimation, and robustness to outliers, the proposed model aimed to obtain and provide a smooth approximation of the underlying distribution of features for training the model. Then these vector spaces are trained by the SVM classifier. Experimental results illustrate the efficacy of the proposed model with accuracy 86.3% and 89.1%, respectively. [ABSTRACT FROM AUTHOR]

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text: We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n ​= ​5), OCT-based (n ​= ​1), and external ocular photo-based (n ​= ​3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79–0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%–68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%–84.00% and specificity of 74.00%–86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions: The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Diabetic retinopathy, a microvascular condition associated with an increased risk of cardiovascular disease, poses a substantial global healthcare challenge. The demand for timely diagnosis has prompted the development of automated solutions due to the scarcity of specialists. In this paper, we introduce a ground-breaking approach to diabetic retinopathy detection – the Diabetic Retinopathy Residual Network (DR-ResNet +). The proposed model leverages the power of deep learning to automatically extract features, achieving optimal results in just seven training epochs. The DR-ResNet + architecture is meticulously designed by incorporating a series of convolutional, pooling, and fully connected layers. Hyperparameter optimization is done using both grid and random search techniques to ensure peak performance. To validate the proposed model's robustness, simulated results are compared with well-established deep learning models, such as GoogleNet, VGG16, and AlexNet, using a comprehensive Kaggle dataset comprising over 35,000 retinal images. Moreover, the proposed model is also tested on external datasets like MESSIDOR and IDRiD for its validation. Simulation results reveal that the proposed DR-ResNet + model not only reduces training time by an impressive 95% but also exhibits outstanding performance metrics, including an accuracy of 0.9898, specificity of 0.9916, precision of 0.9670, sensitivity of 0.9829, and an F1-score of 0.9748. These findings position the proposed model as exceptionally well-suited for real-time clinical applications, offering a potential game-changer in diabetic retinopathy diagnosis. This paper presents DR-ResNet + as a pioneering advancement in diabetic retinopathy diagnosis. With its rapid training, superior accuracy, and significant real-world implications, the model holds promise for transforming the landscape of healthcare by providing timely and precise diagnoses for this critical condition. [ABSTRACT FROM AUTHOR]

Pathologic myopia (PM) is the critical factor of irreversible visual artifacts and puts patients at risk of other severe retinal diseases such as glaucoma. Early intervention can help control the disease's progression and prevent vision loss. Due to its prevalence worldwide, automated detection of PM and its severity is essential. Deep learning-aided diagnosis has proven itself in the field of ophthalmology. The proposed study automatically classifies pathologic and non-pathologic myopia from the fundus images using a guided mini U-Net (GM-U-Net) for feature extraction integrated with a customized convolutional neural network (PMNet) explicitly designed for fundus images. The proposed GM-U-Net allows a deeper network with significantly reduced parameters than conventional U-Net for feature extraction. The proposed PMNet utilizes the features extracted by GM-U-Net to discriminate between PM and a normal retina image. The PMNet classification performance is compared with the other transfer learning models based on the features provided by the GM-U-Net. The combination of GM-U-Net and PMNet outperforms the different models for PM classification. In-depth ablation tests are conducted to realize the current form of PMNet and test its effectiveness. PMNet achieves an accuracy of 90%, average sensitivity of 93%, and specificity of 97% for binary class on the test set, demonstrating it as a valuable tool for early PM detection. Further, to localize the prominent regions in the images, colored heatmap techniques are applied to visualize the affected areas with a hotter color. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD. [ABSTRACT FROM AUTHOR]

The evaluation of Generative Adversarial Networks in the medical domain has shown significant potential for various applications, including adversarial machine learning on medical imaging. This study specifically focuses on assessing the resilience of Convolutional Neural Networks in differentiating between real and Generative Adversarial Network-generated retinal images. The main contributions of this research include the training and testing of Convolutional Neural Networks to evaluate their ability to distinguish real images from synthetic ones. By identifying networks with optimal performances, the study ensures the development of better models for diagnostic classification, enhancing generalization and resilience to adversarial images. Overall, the aim of the study is to demonstrate that the application of Generative Adversarial Networks can improve the resilience of the tested networks, resulting in better classifiers for retinal images. In particular, a network developed by authors, i.e., Standard_CNN, reports the best performance with accuracy equal to 1. [ABSTRACT FROM AUTHOR]

Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size, efficient, and has good prediction quality for biometrics image classification problems is required. Quantizing pre-trained CNN (PCNN) MobileNetV2 architecture combined with a Support Vector Machine (SVM) compacts the model representation and reduces the computational cost and memory requirement. This proposed novel approach combines quantized pre-trained CNN (PCNN) MobileNetV2 architecture with a Support Vector Machine (SVM) to represent models efficiently with low computational cost and memory. Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches, showing the superiority of deep features from MobileNetV2 and SVM classification models, comparing traditional methods, exploring six ocular diseases and normal classification with 20,111 images post-data augmentation, and reducing the number of trainable models. The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration (AMD), one of the most common eye illnesses, Cataract, Diabetes, Glaucoma, Hypertension, and Myopia with one class Normal. From the experiment outcomes, it is observed that the suggested MobileNetV2-SVM model size is compressed. The testing accuracy for MobileNetV2-SVM, InceptionV3, and MobileNetV2 is 90.11%, 86.88%, and 89.76% respectively while MobileNetV2-SVM, InceptionV3, and MobileNetV2 accuracy are observed to be 92.59%, 83.38%, and 90.16%, respectively. The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with Woodpecker Mating Algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties. [ABSTRACT FROM AUTHOR]

Fundus image generation can serve training data diversity as well as clinical and medical education. To solve the problem of the controllability and flexibility of fundus image generation with multiple lesion features, we propose a controllable fundus image generation model (CFIGGAN) based on conditional generative adversarial networks (GAN) for medical data augmentation. The least square loss and the perceptual loss term are added to the final loss to make the generated images more realistic, and the spectral normalization is used as the normalization method of the discriminator to make the training process more stable. In the two-stage training of the model, the vascular tree image concatenates the real and generated images as positive and negative samples to train the model. CFIGGAN can generate diseased fundus images by using the annotations of vascular tree, field of vision(FOV), DR-related lesions as input and controlling the morphology of four types of lesions. Qualitative experimental evaluation shows that the fundus images generated by our model are clear and realistic and close to the real image data distribution. Quantitative experimental evaluation shows that the combination of the spectral norm and the perceptual loss can improve visual observation and quantitative indices, and data augmentation by image generation can further increase the classification accuracy. More importantly, CFIGGAN achieve the controllability of fundus image generation corresponding to DR-related lesions, and the proposed method can be extended to medical images generation of other diseases for broader prospects. [ABSTRACT FROM AUTHOR]

One of the most serious eye illnesses, glaucoma affects the astrocytes and optic nerve fibres, causing irreversible damage to the eyes. As a result, glaucoma early identification is crucial in the medical industry. Retinal image-based detection falls within the category of non-invasive ways of detection among the many techniques. Automatic periodical screening can aid in the prompt detection of retinal glaucoma, while also easing the workload of skilled ophthalmologists. Effective glaucoma treatment can also lessen the severity of vision impairments brought on by the disease's advanced stages. The retinal fundus dataset is used in this paper to undertake a novel glaucoma detection procedure. Additionally, the Scale-invariant feature transform (SIFT) is a widely used method for feature extraction and clustering in picture classification tasks. The characteristic is robust to variations in illumination, noise, partial occlusion, and minor changes in viewpoint in the photos. It is independent of the scale and orientation of the images. The real-time glaucoma screening system is suggested for use with this clustering approach. Convolutional neural network (CNN) classifier optimization is performed using a mix of the SIFT and Rooster Food Search optimization (RFSO) based algorithms for exploration and exploitation procedures. To assess whether a retina is glaucomatous or healthy, the resulting optic disc area is used. As a result, the study was presented based on efficient clustering, precise classification, and the use of various data sets, including the LAG and Rim-One database. The suggested technique was created in MATLAB and tested on this database. The suggested strategy produced accuracy levels above 95% and performed better on comparisons for additional metrics like clustering and classifier-related factors. In order to help researchers conduct additional study on glaucoma detection, we provide research difficulties and their respective solutions. [ABSTRACT FROM AUTHOR]

The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network (CNN) and deep learning to identify the fuzzy weighted regularization between images. This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection. The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System (FES) and Fuzzy differential equation (FDE). The intensities of the different regions in the images and their respective peak levels were determined. Once the peak regions were identified, the recurrence relationships among those peaks were then measured. Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image. Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE. This distinguished between a normal and abnormal eye condition, thus detecting patients with glaucomatous eyes. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes.

Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure. [ABSTRACT FROM AUTHOR]

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

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.

Background: The eyes are the most important part of the human body as these are directly connected to the brain and help us perceive the imagery in daily life whereas, eye diseases are mostly ignored and underestimated until it is too late. Diagnosing eye disorders through manual diagnosis by the physician can be very costly and time taking. Objective: Thus, to tackle this, a novel method namely EyeCNN is proposed for identifying eye diseases through retinal images using EfficientNet B3. Methods: A dataset of retinal imagery of three diseases, i.e. Diabetic Retinopathy, Glaucoma, and Cataract is used to train 12 convolutional networks while EfficientNet B3 was the topperforming model out of all 12 models with a testing accuracy of 94.30%. Results: After preprocessing of the dataset and training of models, various experimentations were performed to see where our model stands. The evaluation was performed using some well-defined measures and the final model was deployed on the Streamlit server as a prototype for public usage. The proposed model has the potential to help diagnose eye diseases early, which can facilitate timely treatment. Conclusion: The use of EyeCNN for classifying eye diseases has the potential to aid ophthalmologists in diagnosing conditions accurately and efficiently. This research may also lead to a deeper understanding of these diseases and it may lead to new treatments. The webserver of EyeCNN can be accessed at (https://abdulrafay97-eyecnn-app-rd9wgz.streamlit.app/). [ABSTRACT FROM AUTHOR]

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels' tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods' results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation ( r 2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results. [ABSTRACT FROM AUTHOR]

Glaucoma stands out as a prevalent ocular ailment in the elderly population, causing substantial harm to the optic nerves and eventual vision impairment. Fundus photography plays a pivotal role in the clinical assessment of glaucoma, facilitating the exploration of associated morphological alterations. Computational algorithms, capable of processing fundus images, have emerged as indispensable tools in this diagnostic domain. Hence, the imperative development of an automated diagnostic system leveraging image processing techniques is underscored. In this study, a novel approach to the segmentation and classification of retinal optic nerve head images is introduced. This method concurrently executes both tasks through a deep learning framework, thereby enhancing the learning speed within the network. The proposed network encompasses approximately 29 million parameters and demonstrates an efficiency of 2.5 seconds for segmenting and classifying retinal images. Central to this strategy is a multi-task deep learning network, harmonizing segmentation and classification processes, and leveraging information from both tasks to optimize learning efficacy. Validation of the proposed method is conducted using the publicly available ORIGA dataset. The attained performance metrics for accuracy, sensitivity, specificity, and F1-score are 99.461, 93.46, 100, and 98.7006, respectively. These results collectively affirm the substantial advancement achieved by the proposed method in comparison to existing methodologies. [ABSTRACT FROM AUTHOR]

Experts require large high-resolution retinal images to detect tiny abnormalities, such as microaneurysms or issues of vascular branches. However, these images often suffer from low quality (e.g., resolution) due to poor imaging device configuration and misoperations. Many works utilized Convolutional Neural Network-based (CNN) methods for image super-resolution. The authors focused on making these models more complex by adding layers and various blocks. It leads to additional computational expenses and obstructs the application in real-life scenarios. Thus, this paper proposes a novel, lightweight, deep-learning super-resolution method for retinal images. It comprises a Vision Transformer (ViT) encoder and a convolutional neural network decoder. To our best knowledge, this is the first attempt to use a transformer-based network to solve the issue of accurate retinal image super-resolution. A progressively growing super-resolution training technique is applied to increase the resolution of images by factors of 2, 4, and 8. The prominent architecture remains constant thanks to the adaptive patch embedding layer, which does not lead to additional computational expense due to increased up-scaling factors. This patch embedding layer includes 2-dimensional convolution with specific values of kernel size and strides that depend on the input shape. This strategy has removed the need to append additional super-resolution blocks to the model. The proposed method has been evaluated through quantitative and qualitative measures. The qualitative analysis also includes vessel segmentation of super-resolved and ground truth images. Experimental results indicate that the proposed method outperforms the current state-of-the-art methods.

Background and purpose: As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs. Methods: Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinicallaboratory signature, were developed to predict the severity of WMLs. Results: Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78. Conclusion: Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels' tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats. [ABSTRACT FROM AUTHOR]

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37. [ABSTRACT FROM AUTHOR]

Glaucoma is a serious threat and it causes blindness and it ranks third in India. Early identification of glaucoma sickness prevents eye disorders from worsening. Due to the need for early illness detection tools to aid in screening and management, retinal image analysis has attracted keen interest. This article presents an automated glaucoma detection system, which is based on machine learning. The retinal images are denoised and contrast enhanced, followed by which the Optic Disc and Cup are extracted. The Complete Local Binary Pattern (CLBP) and contourlet features are extracted to train the Extreme Learning Machine (ELM) classifier. The ELM differentiates between the glaucomatous and non-glaucomatous images. The experimental findings are evaluated with the existing methods, and it is found that the proposed work is superior in terms of standard performance measures. [ABSTRACT FROM AUTHOR]

Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation for the capillaries and the edges of retinal vessels due to thickness inconsistencies and blurry boundaries. In this paper, we propose a novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss (SPNet) to address the above problems. Specifically, we introduce a decoder-sharing mechanism to capture multi-scale semantic information, where feature maps at diverse scales are decoded through a sequence of weight-sharing decoder modules. Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase. A pyramid-like loss function is designed to compensate possible segmentation errors progressively. Experimental results on public benchmarks show that the proposed method outperforms the backbone network and most state-of-the-art methods, especially in the regions of the capillaries and the vessel contours. In addition, performances on cross-datasets verify that SPNet shows stronger generalization ability. [ABSTRACT FROM AUTHOR]

Segmentation of Blood Vessel is a challenging mission in medical image processing to diagnose the disease. It evaluates vessels crucial in automatic retinal vessel extraction with different methodologies, techniques and algorithms to predict the diseases such as Laryngology, neurosurgery and ophthalmology. Using a computer-aided technique, segmentation of blood vessels is conducted in the retina closer to the clinical application routine. This research aims to provide an overview of numerous retinal vessel segmentation approaches, analyse different categories of segmentation techniques, provide a brief description, and evaluate the performance measures. It also reviews, examines, and classifies the procedures, techniques, and methodologies and highlights the important points. The main intention is to provide the reader with a framework for the existing research, introduce the range of retinal vessel segmentation procedures, deliberate the current trends and future directions and summarize the open problems. First, retinal image photography is introduced from the fundus camera. Pre-processing operations and methods of identifying retinal vessels on computer-aided techniques are introduced and discussed to validate results based on the evaluation of various segmentation techniques. The performance of various segmentation techniques and algorithms is estimated using a publicly present database such as DRIVE, STARE, HRF, CHASE, Infant and MESSIDOR. The performance and comparison of various algorithms are assessed in average accuracy, sensitivity, specificity and ROC curves. A huge volume of techniques is considered based on retinal vessel segmentation published in current years. A systematic review is constructed by considering the publications from 2001 to 2021, focusing on methods based on automatic vessel segmentation and classification using fundus camera images. The advantages and limitations are discussed, and tables are included for summarising results at-a-glance. Then an attempt is made to measure the quantitative merit of segmentation methods in terms of accuracy development compared to other methods. Finally, it represented the recent trends with the future direction and summarized the open challenge issues. [ABSTRACT FROM AUTHOR]

To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%. [ABSTRACT FROM AUTHOR]

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels’ tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods’ results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation (r2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results.