Detection Of Retinal Diseases Research Articles

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model's remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment.

Transformer enhanced autoencoder rendering cleaning of noisy optical coherence tomography images.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for detection of retinal diseases, as well as other medical domains. The noise in OCT images presents a great challenge as it hinders the clinician's ability to diagnosis in extensive detail. In this work, a region-based, deep-learning, denoising framework is proposed for adaptive cleaning of noisy OCT-acquired images. The core of the framework is a hybrid deep-learning model named transformer enhanced autoencoder rendering (TEAR). Attention gates are utilized to ensure focus on denoising the foreground and to remove the background. TEAR is designed to remove the different types of noise artifacts commonly present in OCT images and to enhance the visual quality. Extensive quantitative evaluations are performed to evaluate the performance of TEAR and compare it against both deep-learning and traditional state-of-the-art denoising algorithms. The proposed method improved the peak signal-to-noise ratio to 27.9dB, CNR to 6.3dB, SSIM to 0.9, and equivalent number of looks to 120.8dB for a dental dataset. For a retinal dataset, the performance metrics in the same sequence are: 24.6, 14.2, 0.64, and 1038.7dB, respectively. The results show that the approach verifiably removes speckle noise and achieves superior quality over several well-known denoisers.

Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images

BackgroundArtificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on...

Multimodal Imaging Evaluation of Rabbit Models

Currently available imaging modalities each have limitations in their scope, preventing a comprehensive view of the eye. A multimodal system can simultaneously use numerous modalities to increase the effectiveness of imaging to provide structural and functional information, while decreasing the time and invasiveness of multiple procedures. In this study, optical coherence tomography (OCT), photoacoustic microscopy (PAM), fundus photography, fluorescein angiography (FA), and indocyanine green angiography (ICGA) were performed on a baseline rabbit model to verify the imaging system. Clear images distinguishing retinal vessels, choroidal vessels, and retinal layers were obtained, verifying the multimodal system’s efficacy before imaging experimental models. Imaging macular degeneration and glaucoma animal models can advance understanding of novel treatments in ophthalmic research. Multimodal imaging also offers a promising new means for early detection of retinal diseases in clinical settings.

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

Study on Limited Data Problem - Semantic Segmentation of Retinal Fundus Images

Medical images captured through various imaging modalities are key to the diagnosis and treatment of diseases, and these images serve as a major input to AI driven models but due to the scarcity of medical experts and their quality research time, the quantum of data required for AI models is limited. Moreover, medical image analysis is a laborious and error prone task. Early detection of retinal diseases by means of AI based semantic segmentation model has been boon to the diagnostic system. However, performing semantic segmentation with limited data of retinal fundus image is quite challenging. The present study is primarily focused on investigation of effectiveness of data augmentation with gamma corrected images to alleviate the problem of limited annotated data in deep learning models. The most widely accepted U-Net model in medical domain for image segmentation is trained employing retinal fundus image dataset augmented with gamma corrected images on all image channels and then tested against major publicly available datasets. The proposed method has outperformed other complex contemporary methods in terms of sensitivity and has also shown better generalizability across datasets from different institutions.

Advanced Detection of Retinal Diseases via Novel Hybrid Deep Learning Approach

Advanced Detection of Retinal Diseases via Novel Hybrid Deep Learning Approach

Enhancing medical image analysis: A fusion of fully connected neural network classifier with CNN-VIT for improved retinal disease detection

Diseases of the retina continue to be a leading cause of blindness and visual impairment around the world. In the field of medical image analysis, specifically retinal disease identification, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have showed remarkable potential. In this paper, we present a unique method for detecting retinal diseases by combining the advantages of the Inception-V3, ResNet-50, and Vision Transformer architectures into a single model called a Cascade CNN-ViT. The suggested Cascade CNN-ViT model extracts local features from retinal pictures by leveraging the spatial hierarchy learning capabilities of Inception-V3 and ResNet-50. The Vision Transformer takes these regional characteristics and uses self-attention mechanisms to pick up global context information and long-range interdependence. The model successfully combines fine-grained local information with semantically significant global contextual cues by merging the output representations from the CNNs and Vision Transformer. undertaking comprehensive experiments on a large and varied dataset of multimodal retinal pictures to evaluate the performance of the proposed technique. Cascade CNN-ViT model outperforms standalone CNNs and Vision Transformers, as shown by the experimental findings. The model is also resilient across all classes of retinal diseases and is able to successfully deal with the complications introduced by using multiple picture types. Overall, the power of cascading Inception-V3, ResNet-50, and Vision Transformer topologies for improved retinal illness diagnosis has been demonstrated. Potentially improving the management of retinal illnesses and preserving visual health, the proposed approach could have important consequences for early detection and timely intervention.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.

Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosingretinal diseases using optical coherence tomography (OCT) images. This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Cellular-Level Analysis of Retinal Blood Vessel Walls Based on Phase Gradient Images.

Diseases such as diabetes affect the retinal vasculature and the health of the neural retina, leading to vision problems. We describe here an imaging method and analysis procedure that enables characterization of the retinal vessel walls with cellular-level resolution, potentially providing markers for eye diseases. Adaptive optics scanning laser ophthalmoscopy is used with a modified detection scheme to include four simultaneous offset aperture channels. The magnitude of the phase gradient derived from these offset images is used to visualize the structural characteristics of the vessels. The average standard deviation image provides motion contrast and enables segmentation of the vessel lumen. Segmentation of blood vessel walls provides quantitative measures of geometrical characteristics of the vessel walls, including vessel and lumen diameters, wall thickness, and wall-to-lumen ratio. Retinal diseases may affect the structural integrity of the vessel walls, their elasticity, their permeability, and their geometrical characteristics. The ability to measure these changes is valuable for understanding the vascular effects of retinal diseases, monitoring disease progression, and drug testing. In addition, loss of structural integrity of the blood vessel wall may result in microaneurysms, a hallmark lesion of diabetic retinopathy, which may rupture or leak and further create vision impairment. Early identification of such structural abnormalities may open new treatment avenues for disease management and vision preservation. Functional testing of retinal circuitry through high-resolution measurement of vasodilation as a response to controlled light stimulation of the retina (neurovascular coupling) is another application of our method and can provide an unbiased evaluation of one's vision and enable early detection of retinal diseases and monitoring treatment results.

Lesion Detection in Optical Coherence Tomography with Transformer-Enhanced Detector.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for the detection of retinal diseases and in dentistry for the early detection of tooth decay. Speckle noise is ubiquitous in OCT images, which can hinder diagnosis by clinicians. In this paper, a region-based, deep learning framework for the detection of anomalies is proposed for OCT-acquired images. The core of the framework is Transformer-Enhanced Detection (TED), which includes attention gates (AGs) to ensure focus is placed on the foreground while identifying and removing noise artifacts as anomalies. TED was designed to detect the different types of anomalies commonly present in OCT images for diagnostic purposes and thus aid clinical interpretation. Extensive quantitative evaluations were performed to measure the performance of TED against current, widely known, deep learning detection algorithms. Three different datasets were tested: two dental and one CT (hosting scans of lung nodules, livers, etc.). The results showed that the approach verifiably detected tooth decay and numerous lesions across two modalities, achieving superior performance compared to several well-known algorithms. The proposed method improved the accuracy of detection by 16-22% and the Intersection over Union (IOU) by 10% for both dentistry datasets. For the CT dataset, the performance metrics were similarly improved by 9% and 20%, respectively.

MBT: Model-Based Transformer for retinal optical coherence tomography image and video multi-classification

Background and objectiveThe detection of retinal diseases using optical coherence tomography (OCT) images and videos is a concrete example of a data classification problem. In recent years, Transformer architectures have been successfully applied to solve a variety of real-world classification problems. Although they have shown impressive discriminative abilities compared to other state-of-the-art models, improving their performance is essential, especially in healthcare-related problems. MethodsThis paper presents an effective technique named model-based transformer (MBT). It is based on popular pre-trained transformer models, particularly, vision transformer, swin transformer for OCT image classification, and multiscale vision transformer for OCT video classification. The proposed approach is designed to represent OCT data by taking advantage of an approximate sparse representation technique. Then, it estimates the optimal features, and performs data classification. ResultsThe experiments are carried out using three real-world retinal datasets. The experimental results on OCT image and OCT video datasets show that the proposed method outperforms existing state-of-the-art deep learning approaches in terms of classification accuracy, precision, recall, and f1-score, kappa, AUC-ROC, and AUC-PR. It can also boost the performance of existing transformer models, including Vision transformer and Swin transformer for OCT image classification, and Multiscale Vision Transformers for OCT video classification. ConclusionsThis work presents an approach for the automated detection of retinal diseases. Although deep neural networks have proven great potential in ophthalmology applications, our findings demonstrate for the first time a new way to identify retinal pathologies using OCT videos instead of images. Moreover, our proposal can help researchers enhance the discriminative capacity of a variety of powerful deep learning models presented in published papers. This can be valuable for future directions in medical research and clinical practice.

Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection.

The current advancement towards retinal disease detection mainly focused on distinct feature extraction using either a convolutional neural network (CNN) or a transformer-based end-to-end deep learning (DL) model. The individual end-to-end DL models are capable of only processing texture or shape-based information for performing detection tasks. However, extraction of only texture- or shape-based features does not provide the model robustness needed to classify different types of retinal diseases. Therefore, concerning these two features, this paper developed a fusion model called 'Conv-ViT' to detect retinal diseases from foveal cut optical coherence tomography (OCT) images. The transfer learning-based CNN models, such as Inception-V3 and ResNet-50, are utilized to process texture information by calculating the correlation of the nearby pixel. Additionally, the vision transformer model is fused to process shape-based features by determining the correlation between long-distance pixels. The hybridization of these three models results in shape-based texture feature learning during the classification of retinal diseases into its four classes, including choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL. The weighted average classification accuracy, precision, recall, and F1 score of the model are found to be approximately 94%. The results indicate that the fusion of both texture and shape features assisted the proposed Conv-ViT model to outperform the state-of-the-art retinal disease classification models.

Multi-label Retinal Disease Classification Using Transformers.

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.

A Hybrid R-FTCNN based on principal component analysis for retinal disease detection from OCT images

Retinal disorders are of paramount importance in human life and if they do not receive treatment for retinal disorders, humans may undergo visual complications. The physician may diagnose more easily with optical coherence tomography (OCT) in ophthalmology, where early diagnosis is essential. To date, artificial intelligence has recently been implemented more and more frequently in the field of health thanks to technological advancements. In this study, a hybrid Retinal Fine Tuned Convolutional Neural Network (R-FTCNN) has been proposed for the detection of retinal diseases such as diabetic macular edema, drusen, and choroidal neovascularization from OCT images. In this study, the newly constructed R-FTCNN architecture and principal component analysis (PCA) are utilized concurrently as part of this methodology. PCA was used to convert the fully connected layers of the R-FTCNN to principal components, and the Softmax function was applied to the principal components to construct a new classification model. By creating various feature arrays from fully connected layers of R-FTCNN, ablation test was applied and FC layer (or combinations) with the best performance was determined. Afterwards, the FC layer with highest performance was used as the basis for the proposed methodology. Leveraging OCT datasets from Duke and California San Diego University (UCSD), which are often cited in the literature, the performance of the suggested technique was assessed. This technique had 100% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% F1 score metric for classifying retinal disorders in the Duke dataset. Furthermore, this technique demonstrated 99.70% accuracy, 99.70% sensitivity, 99.90% specificity, 99.70% precision, and 99.70% F1 score metrics when classifying retinal disorders in the UCSD dataset. ROC curves were generated for the performance summary of the proposed method on two OCT datasets, and the area under the curve (AUC) metrics revealed that the suggested approach performed well in terms of classifications. In this study, pre-trained CNN architectures namely GoogLeNet, AlexNet, and ResNet (18, 50 and 101) were employed to compare the performance of the proposed technique. The proposed method outperformed state-of-the-art technology when conclusions from two OCT datasets were compared with those from past investigations. In conclusion, the proposed framework might aid in the creation of computer-aided diagnostic instruments for the early detection of retinal disorders in ophthalmology.

Domain Adaptation-Based Automated Detection of Retinal Diseases from Optical Coherence Tomography Images

Purpose To verify the effectiveness of domain adaptation in generalizing a deep learning-based anomaly detection model to unseen optical coherence tomography (OCT) images. Methods Two datasets (source and target, where labelled training data was only available for the source) captured by two different OCT facilities were collected to train the model. We defined the model containing a feature extractor and a classifier as Model One and trained it with only labeled source data. The proposed domain adaptation model was defined as Model Two, which has the same feature extractor and classifier as Model One but has an additional domain critic in the training phase. We trained the Model Two with both the source and target datasets; the feature extractor was trained to extract domain-invariant features while the domain critic learned to capture the domain discrepancy. Finally, a well-trained feature extractor was used to extract domain-invariant features and a classifier was used to detect images with retinal pathologies in the two domains. Results The target data consisted of 3,058 OCT B-scans captured from 163 participants. Model One achieved an area under the curve (AUC) of 0.912 [95% confidence interval (CI), 0.895–0.962], while Model Two achieved an overall AUC of 0.989 [95% CI, 0.982–0.993] for detecting pathological retinas from healthy samples. Moreover, Model Two achieved an average retinopathies detection accuracy of 94.52%. Heat maps showed that the algorithm focused on the area with pathological changes during processing, similar to manual grading in daily clinical work. Conclusions The proposed domain adaptation model showed a strong ability in reducing the domain distance between different OCT datasets.

AI telemedicine screening in ophthalmology: health economic considerations

AI telemedicine screening in ophthalmology: health economic considerations

Game Theory-Based Dynamic Weighted Ensemble for Retinal Disease Classification

An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extraction-based machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model. The ensemble approach significantly improves the accuracy of retinal disease prediction and results in an excellent performance when compared to the individual deep learning and feature-based models.

Arterial hypertension and retinal layer thickness: the Beijing Eye Study

PurposeTo investigate relationships between blood pressure and the thickness of single retinal layers in the macula.MethodsParticipants of the population-based Beijing Eye Study, free of retinal or optic nerve disease, underwent...

LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis

Automatic detection of retinal diseases based on deep learning technology and Ultra-widefield (UWF) images plays an important role in clinical practices in recent years. However, due to small lesions and limited data samples, it is not easy to train a detection-accurate model with strong generalization ability. In this paper, we propose a lesion attention conditional generative adversarial network (LAC-GAN) to synthesize retinal images with realistic lesion details to improve the training of the disease detection model. Specifically, the generator takes the vessel mask and class label as the conditional inputs, and processes the random Gaussian noise by a series of residual block to generate the synthetic images. To focus on pathological information, we propose a lesion feature attention mechanism based on random forest (RF) method, which constructs its reverse activation network to activate the lesion features. For discriminator, a weight-sharing multi-discriminator is designed to improve the performance of model by affine transformations. Experimental results on multi-center UWF image datasets demonstrate that the proposed method can generate retinal images with reasonable details, which helps to enhance the performance of the disease detection model.