Classification Of Fundus Images Research Articles

Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach

Background Early diagnosis and treatment of diabetic eye disease (DED) improve prognosis and lessen the possibility of permanent vision loss. Screening of retinal fundus images is a significant process widely employed for diagnosing patients with DED or other eye problems. However, considerable time and effort are required to detect these images manually. Methods Deep learning approaches in machine learning have attained superior performance for the binary classification of healthy and pathological retinal fundus images. In contrast, multi-class retinal eye disease classification is still a difficult task. Therefore, a two-phase transfer learning approach is developed in this research for automated classification and segmentation of multi-class DED pathologies. Results In the first step, a Modified ResNet-50 model pre-trained on the ImageNet dataset was transferred and learned to classify normal diabetic macular edema (DME), diabetic retinopathy, glaucoma, and cataracts. In the second step, the defective region of multiple eye diseases is segmented using the transfer learning-based DenseUNet model. From the publicly accessible dataset, the suggested model is assessed using several retinal fundus images. Our proposed model for multi-class classification achieves a maximum specificity of 99.73%, a sensitivity of 99.54%, and an accuracy of 99.67%.

Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network

Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network

A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

Cataracts are common eye disorders characterized by the clouding of the lens, preventing light from passing through and impairing vision. Various factors, including changes in the lens’s hydration or alterations in its proteins, may contribute to their development. Regular eye examinations conducted by an ophthalmologist or optometrist are imperative for detecting cataracts and other ocular conditions early on. Manual checks by caregivers pose several problems, including subjectivity, human error, and a lack of expertise. Biomedical fusion involves combining or linking various characteristics specific to certain diseases from different medical imaging resources. The primary objectives of this approach in disease classification are to reduce the error rate and increase the number of retrieved features. The aim of this study is to evaluate the outcomes associated with fusing visual features related to left and right eye cataract characteristics. Additionally, we investigate the impact of limited variability in deep learning models, specifically in the classification of cataract fundus versus normal fundus images. To address this issue, this study introduces CataractNetDetect, an innovative multi-label deep learning classification system that fuses feature representations from pairs of fundus images (e.g., left and right eyes) for the automatic diagnosis of various ocular disorders. Our focus is on achieving improved performance by stacking discriminative deep feature representations to combine two fundus images into a unified feature representation. Several deep learning architectures are utilized as feature descriptors, including ResNet-50, DenseNet-121, and Inception-V3, enhancing the resilience and quality of representations. Fine-tuning of these DL architectures is conducted using the ImageNet dataset, followed by an integrated stacking approach combining ResNet-50, DenseNet-121, and Inception-V3 models. The model is trained on the publicly available ODIR-5k dataset, which includes 5000 left/right eye images depicting eight different ocular conditions, ranging from healthy states to uncommon ailments such as cataracts, glaucoma, age-related macular degeneration (AMD), diabetes, hypertension, and myopia abnormalities. Moreover, extensive preprocessing of the images is performed, including data augmentation, noise reduction, contrast enhancement, scaling, and circular border cropping. The CataractNetDetect system demonstrates F1-scores, AUC, and maximum validation scores of 98.0%, 97.9%, and 100%, respectively. This ensemble-based model distinguishes itself by surpassing the performance of conventional established methodologies, including ResNet-50, DenseNet-121, and Inception-V3, thereby underscoring its efficacy in diagnostic applications.

Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi

ObjectiveThe purpose of this study is to summarize the results from 3 experimental studies into the use of artificial intelligence to classify and segment colour fundus images with choroidal nevi. Study DesignThis study is based on a secondary analysis of colour fundus images taken of patients receiving usual clinical care from the Alberta Ocular Brachytherapy Program. MethodsHigh-resolution colour fundus images were labeled by experienced ocular oncologists. In experimental study 1, four pre-trained models (ResNet 50, VGG-19, VGG-16, and AlexNet) were evaluated for their ability to classify images based on the presence of choroidal nevi. In experimental study 2, the performance of 3 patch-based models to classify images based on the presence of choroidal nevi were compared. In experimental study 3, four convolutional neural network models were developed to segment the images. In experimental studies 1 and 2, performance was measured using accuracy, precision, recall, F1 score, and AUC. In experimental study 3, IoU and Dice measures were used to evaluate performance. ResultsA total of 591 labelled colour fundus images were used for analysis. In experimental study 1, VGG-16 showed the best accuracy, AUC, and recall, but lower precision in classifying images. In experimental study 2, the patched approached enhanced with artifact and contrast outperformed the others in classifying images. In experimental study 3, a voting-based Ensemble model excelled in segmenting the part of images with nevi. ConclusionsIt is feasible to train AI models to identify choroidal nevi in colour fundus images.

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

Enhancing retinal fundus image classification through Active Gradient Deep Convolutional Neural Network and Red Spider Optimization

Enhancing retinal fundus image classification through Active Gradient Deep Convolutional Neural Network and Red Spider Optimization

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through fundus images is age-related macular degeneration, which initially manifests as the appearance of lesions called drusen. Several ways of pre-diagnosing macular degeneration have been proposed, methods based entirely on the segmentation of drusen with prior image processing have been designed and applied, and methods based on image pre-processing and subsequent conversion to feature vectors, or patterns, to be classified by a Machine-Learning model have also been developed. Finally, in recent years, the use of Deep-Learning models, particularly Convolutional Networks, has been proposed and used in classification problems where the data are only images. The latter has allowed the so-called transfer learning, which consists of using the learning achieved in the solution of one problem to solve another. In this paper, we propose the use of transfer learning through the Xception Deep Convolutional Neural Network to detect age-related macular degeneration in fundus images. The performance of the Xception model was compared against six other state-of-the-art models with a dataset created from images available in public and private datasets, which were divided into training/validation and test; with the training/validation set, the training was made using 10-fold cross-validation. The results show that the Xception neural network obtained a validation accuracy that surpasses other models, such as the VGG-16 or VGG-19 networks, and had an accuracy higher than 80% in the test set. We consider that the contributions of this work include the use of a Convolutional Neural Network model for the detection of age-related macular degeneration through the classification of fundus images in those affected by AMD (drusen) and the images of healthy patients. The performance of this model is compared against other methods featured in the state-of-the-art approaches, and the best model is tested on a test set outside the training and validation set.

Automated machine learning model for fundus image classification by health-care professionals with no coding experience

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CFDL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.

An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis

Eye problems can lead to vision loss and have a significant impact on daily life, underscoring the critical importance of early diagnosis and treatment to prevent further damage and complications. A comprehensive segmentation-classification framework – VisionDeep-AI is developed in the proposed work for retinal vessel segmentation and multi-class classification on given fundus images. A weighted bi-directional feature pyramid network and U-Net backbone architecture-based customized deep-learning model is built to segment blood vessels which enhances feature extraction and multi-scale feature fusion. The architectural design comprises an end-to-end encoder-decoder network featuring six-depth layers with varying resolutions, enabling the extraction of high-level descriptors and lower-level, fine-grained characteristics. Furthermore, a multi-modal deep feature fusion architecture is developed for the multi-class classification of fundus images in four different categories by integrating features from segmented vessel images and raw fundus images, thereby accommodating more diversified information. A thorough analysis of VisionDeep-AI has been done on a comprehensive colour fundus image dataset. To ensure reliable results, data augmentation was done to prevent the over-fitting of the model and to enhance its generalization capability. The segmentation model performed exceptionally well and achieved a high accuracy of 97.73% and a dice coefficient of 89.90% in blood vessel segmentation from fundus images. Furthermore, multi-modal deep feature-fused classification architecture achieved a test accuracy of 81.50%, with a specificity of 93.83%. These findings showcase the robustness, generalizability and efficiency of the proposed VisionDeep-AI framework for fundus image segmentation and retinal disease classification, contributing towards advanced medical technologies and diagnosis precision.

The classification of eye diseases from fundus images based on CNN and pretrained models

Visual impairment affects more than a billion people worldwide due to insufficient care or inadequate vision screening. Computer-aided diagnosis using deep neural networks is a promising approach, it can analyse and process retinal fundus images, providing valuable reference data for doctors in clinical diagnosis or screening. This study aims to achieve an accurate classification of fundus images, including images of healthy patients as well as those with diabetic retinopathy, cataracts, and glaucoma, using a convolutional neural network (CNN) architecture and several pretrained models (AlexNet, GoogleNet, ResNet18, ResNet50, YOLOv3, and VGG 19). To enhance the training process, a mirror effect technique was applied to augment the volume of data. The experimental study resulted in very satisfactory outcomes, with the GoogleNet model paired with the SGDM optimiser achieving the highest accuracy (92.7 %).

On Performance Analysis Of Diabetic Retinopathy Classification

This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.

Precise lesion analysis to detect diabetic retinopathy using Generative Adversarial Network(GAN) and Mask-RCNN

Modern Medical diagnosis has a significant reliance on systematic image analysis for rational prognosis. With the advancement of computer vision methods, supported by deep learning algorithms, the non-invasive and early-stage detection of various diseases is a significant area of research. Amongst various image-based disease identification scopes, in this paper it is focused on Diabetic Retinopathy (DR), which is an eye disease caused by diabetes that can even lead to blindness. Therefore, early detection is critical to prevent visual disturbances from such diseases. In this study, the objective is to identify diabetic retinopathy by analysing fundus images using deep learning methods. Identifying diabetic retinopathy from fundus images, requires considerable improvement of image quality. In this work the retina images obtained by the fundus camera is improved by Generative Adversarial Network(GAN) for specific analysis. This study aimed to detect lesions using mask-RCNN techniques, where pretrained models such as R-50, R-101, and X-101 are utilized for robust segmentation and masking of the damaged regions of the fundus images. As the lesions are pathologically classified into two categories: ’exudates’ and ’microaneurysms’, similarly the input images are also mapped into two categories. Evaluation is based on the mean average precision (MAP) and the findings infer the conclusion. The comprehensive set of real-life retina images obtained from the clinical dataset benchmarked with the proposed mechanism and it is found that the proper segmentation and improvisation of fundus images by applying GAN have considerably enhanced the degree of accuracy in the Region-of-interest(ROI) bounding box for exact identification of Diabetic Retinopathy, along with reporting on stage of the disease. The aim of the work is well established in implementing the algorithm and finding the result for mapping it into the automated classification of fundus images in batches where the X-101 model seen to perform better in terms of finding Bounding Box Average Precision for both exudates (75.20%) and microaneurysms (67.202%) followed by Segmented Region Average Precision for exudates (62.363%) and microaneurysms (57.690%). The obtained results may reduce human intervention and classify a large number of input images in field-level examination in a faster manner.

Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification.

Fundus disease is a complex and universal disease involving a variety of pathologies. Its early diagnosis using fundus images can effectively prevent further diseases and provide targeted treatment plans for patients. Recent deep learning models for classification of this disease are gradually emerging as a critical research field, which is attracting widespread attention. However, in practice, most of the existing methods only focus on local visual cues of a single image, and ignore the underlying explicit interaction similarity between subjects and correlation information among pathologies in fundus diseases. In this paper, we propose a novel label-aware dual graph neural networks for multi-label fundus image classification that consists of population-based graph representation learning and pathology-based graph representation learning modules. Specifically, we first construct a population-based graph by integrating image features and non-image information to learn patient's representations by incorporating associations between subjects. Then, we represent pathologies as a sparse graph where its nodes are associated with pathology-based feature vectors and the edges correspond to probability of the co-occurrence of labels to generate a set of classifier scores by the propagation of multi-layer graph information. Finally, our model can adaptively recalibrate multi-label outputs. Detailed experiments and analysis of our results show the effectiveness of our method compared with state-of-the-art multi-label fundus image classification methods.

Fundus Image Classification for the Early Detection of Issues in the DR for the Effective Disease Diagnosis

Diabetic Retinopathy and fundus images, “DR” could also refer to Digital Retinography, which involves the digital imaging of the retina. Digital retinography allows for the storage, retrieval, and transmission of retinal images for analysis and diagnosis. It plays a crucial role in screening for diabetic retinopathy and other eye diseases. The use of artificial intelligence and machine learning algorithms in the analysis of fundus images has gained traction in recent years. These technologies can assist healthcare professionals in the early detection and monitoring of diabetic retinopathy by analyzing patterns and abnormalities in the retinal images. This paper introduces a novel approach to image analysis, combining Local Semantic Object Feature Extraction (LsOFE) and Gray Level Co-occurrence Matrix (GLCM) metrics for robust object recognition and classification. The LsOFE process captures intricate local features, enhancing the representation of objects in images. The subsequent GLCM-based feature extraction provides detailed insights into texture characteristics. The final classification step, utilizing LsOFE features, demonstrates high precision, recall, and F1-Score for most images, showcasing the effectiveness of the proposed methodology. However, challenges are identified in the classification of specific images, suggesting areas for improvement and refinement. Overall, this research contributes a valuable framework for image processing, with the potential for broader applications in fields such as computer vision and pattern recognition. The results presented underscore the promising performance of the proposed approach, offering a foundation for further exploration and optimization in the realm of image analysis.

A Hybrid Diabetic Retinopathy Neural Network Model for Early Diabetic Retinopathy Detection and Classification of Fundus Images

A Hybrid Diabetic Retinopathy Neural Network Model for Early Diabetic Retinopathy Detection and Classification of Fundus Images

Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images

Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.

Multiclass multilabel ophthalmological fundus image classification based on optimised deep feature space evolutionary model

Multiclass multilabel ophthalmological fundus image classification based on optimised deep feature space evolutionary model

Retinal fundus image classification for diabetic retinopathy using transfer learning technique

Retinal fundus image classification for diabetic retinopathy using transfer learning technique

Lesion identification in fundus images via convolutional neural network-vision transformer

Purpose:Classification of fundus lesions plays a vital role in detecting some diseases in their early stages, including glaucoma and diabetes. During the clinical diagnosis and treatment prognosis, its higher accuracy will assist the doctors in optimizing the therapeutic schedule and reduce the workload of the ophthalmologists. However, the intrinsic characteristics of retinal lesions in retinal images make the detection process challenging. Methods:In recent decades, many deep learning algorithms have been widely applied in various areas and achieved promising outcomes. Among the deep learning approaches, due to the overall success of Transformers in the natural language processing field, plenty of researchers have begun to explore the applicability of Transformer models in clinical applications such as recognizing various ophthalmic diseases. In this study, we propose a vision transformer-based pipeline for accurately classifying retinal diseases. To fully exploit the local and global associations between the individual image patches, a convolutional neural network concatenated with a vision transformer is employed to form the proposed framework. It can improve the performance of retinal lesion classification compared with a single-vision transformer or a convolutional neural network. Results:Moreover, we pre-train our proposed framework on the ImageNet ISLVRC dataset and a sizeable retinal image database in sequence. Then, we fine-tune this vision transformer on downstream fundus image classification tasks. Conclusion:Experimental results demonstrate that the proposed approach achieves superior performance on two publicly available datasets over state-of-the-art deep learning-based techniques, including convolutional neural networks and other vision transformers.