Detection Of Retinal Diseases Research Articles

A Comprehensive Study of Supervised Learning Models on the OCTID Dataset for Retinal Disease Detection with Future Directions

This paper presents a critical analysis of recent approaches to supervised learning techniques on the OCTID database that are important in the early diagnosis of retinal diseases such as AMD, CSR, DME, and MH. The review concentrates on methodologies, experimental configurations, and assessments of various machine learning and deep learning techniques. The limitations observed in the current work are in terms of computational complexity, possible limitation in labeling, and lack of extensive experimentation using videos with OCT. For future work, we suggest using semi-supervised learning as they are more accurate and less costly in medical applications.

Deep Learning-Based Detection of Diabetic Retinopathy using Retina Images

The early detection of retinal diseases, such as diabetic retinopathy, is essential to prevent irreversible vision loss. In this study, we propose an automated system for the detection of retinal diseases using deep learning techniques, specifically Convolutional Neural Networks (CNN) and pre-trained models like MobileNet and VGG16. These models are applied to retinal fundus images to identify abnormalities, such as microaneurysms and hemorrhages, that are indicative of retinal diseases. The use of CNN allows for efficient feature extraction, while MobileNet and VGG16, known for their strong performance in image classification tasks, enable accurate disease classification across different stages. The system is trained and evaluated on publicly available datasets, ensuring robust performance across diverse retinal images. The study compares the performance of both MobileNet and VGG16 models, with a focus on achieving high accuracy, sensitivity, and specificity in detecting retinal abnormalities. MobileNet, with its lightweight architecture, proves advantageous for real-time applications on mobile devices, offering fast and efficient disease detection. On the other hand, VGG16 delivers higher precision but at a greater computational cost. Experimental results demonstrate the system’s potential to assist healthcare professionals by automating the diagnostic process, enabling early detection and timely treatment of retinal diseases. This approach significantly reduces the reliance on manual screening, leading to more accessible and scalable diagnostic solutions

Multi-resolution visual Mamba with multi-directional selective mechanism for retinal disease detection.

Retinal diseases significantly impact patients' quality of life and increase social medical costs. Optical coherence tomography (OCT) offers high-resolution imaging for precise detection and monitoring of these conditions. While deep learning techniques have been employed to extract features from OCT images for classification, convolutional neural networks (CNNs) often fail to capture global context due to their focus on local receptive fields. Transformer-based methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies. To overcome these limitations, we introduce the Multi-Resolution Visual Mamba (MRVM) model, which addresses long-range dependencies with linear computational complexity for OCT image classification. The MRVM model initially employs convolution to extract local features and subsequently utilizes the retinal Mamba to capture global dependencies. By integrating multi-scale global features, the MRVM enhances classification accuracy and overall performance. Additionally, the multi-directional selection mechanism (MSM) within the retinal Mamba improves feature extraction by concentrating on various directions, thereby better capturing complex, orientation-specific retinal patterns. Experimental results demonstrate that the MRVM model excels in differentiating retinal images with various lesions, achieving superior detection accuracy compared to traditional methods, with overall accuracies of 98.98\% and 96.21\% on two public datasets, respectively. This approach offers a novel perspective for accurately identifying retinal diseases and could contribute to the development of more robust artificial intelligence algorithms and recognition systems for medical image-assisted diagnosis.

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.

EfficientNet-Based Model for Automated Classification of Retinal Diseases Using Fundus Images

The automated classification of retinal diseases, such as diabetic retinopathy, cataract, glaucoma, and normal retinal conditions, is critical for early diagnosis and timely intervention. This study employs an EfficientNet-based convolutional neural network (CNN) to classify these diseases using a dataset of fundus images, achieving an accuracy of 93.8%. By leveraging transfer learning and fine-tuning techniques, our model is both computationally efficient and highly accurate, making it suitable for clinical applications. In comparison to other CNN-based approaches and ensemble methods, as explored in recent studies, EfficientNet offers a balanced performance in terms of precision, recall, and inference speed, which is crucial in ophthalmic diagnostics. Previous research on real-time object detection in medical imaging, such as Billah et al.’s studies on YOLO models and ensemble methods, has demonstrated the effectiveness of CNNs for accurate classification across various medical imaging tasks. This paper situates EfficientNet within these advancements, underscoring its potential for high-accuracy, multi-class classification in retinal disease detection. Our findings suggest that EfficientNet can play a significant role in automating retinal disease screening, contributing to improved patient outcomes and facilitating the integration of AI in ophthalmology.

Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and age-related macular degeneration

ABSTRACT Clinical Relevance The challenges of establishing retinal screening programs in rural settings may be mitigated by the emergence of deep-learning systems for early disease detection. Background Deep-learning systems have demonstrated promising results in retinal disease detection and may be particularly useful in rural settings where accessibility remains a barrier to equitable service provision. This study aims to evaluate the real-world performance of Thirona RetCAD for the detection of referable diabetic retinopathy and age-related macular degeneration in a rural Australian population. Methods Colour fundus images from participants with known diabetic retinopathy or age-related macular degeneration were randomly selected from ophthalmology clinics in four rural Australian centres. Grading was confirmed retrospectively by two retinal specialists. RetCAD produced a quantitative measure (0–100) for DR and AMD severity. The area under the ROC curve (AUC) was calculated. Sensitivity, specificity, and positive and negative predictive values were calculated at a pre-defined cut-point of ≥50. Results A total of 150 images from 82 participants were included. The mean age (SD) was 64.0 (12.8) years. Seventy-nine (52.7%) eyes had evidence of referable DR, while 54 (36.0%) had evidence of referable AMD. The AUC for referable DR detection was 0.971 (95% CI 0.950–0.936) with a sensitivity of 86.1% (76.8%–92.0%) and a specificity of 91.6% (82.8%−96.1%) at the pre-defined cut-point. Using the Youden Index method, the optimal cut-point was 41.2 (sensitivity 93.7%, specificity 90.1%). The AUC for the detection of referable AMD was 0.880 (0.824–0.936). At the pre-defined cut-point sensitivity was 88.9% (77.8%–94.8%) and specificity was 66.7% (56.8%–75.3%). The optimal cut-point was 52.6 (sensitivity 87.0%, specificity 75.0%). Conclusion RetCAD is comparable with but does not outperform equivalent deep-learning systems for retinal disease detection. RetCAD may be suitable as an automated screening tool in a rural Australian setting.

Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.

An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.

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.

Deep learning based retinal vessel segmentation and hypertensive retinopathy quantification using heterogeneous features cross-attention neural network.

Retinal vessels play a pivotal role as biomarkers in the detection of retinal diseases, including hypertensive retinopathy. The manual identification of these retinal vessels is both resource-intensive and time-consuming. The fidelity of vessel segmentation in automated methods directly depends on the fundus images' quality. In instances of sub-optimal image quality, applying deep learning-based methodologies emerges as a more effective approach for precise segmentation. We propose a heterogeneous neural network combining the benefit of local semantic information extraction of convolutional neural network and long-range spatial features mining of transformer network structures. Such cross-attention network structure boosts the model's ability to tackle vessel structures in the retinal images. Experiments on four publicly available datasets demonstrate our model's superior performance on vessel segmentation and the big potential of hypertensive retinopathy quantification.

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.