Classification Of Retinal Images Research Articles

OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification

OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification

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

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. 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. 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%.

MobilenetV2-RC: A lightweight network model for retinopathy classification in retinal OCT images

Abstract Retinopathy is an important ophthalmic disease that causes blindness in the elderly population. With the global old peoples increasing, the ophthalmic healthcare system is more and significant to pre-diagnosis. Optical Coherence Tomography (OCT) is considered the gold standard for ophthalmic treatment and diagnosis. OCT technologies and equipment continue to develop towards the intelligence and convenience for requirements of rapid diagnosis in the remote and poverty-stricken areas. We proposed an improved MobilenetV2 lightweight model for retinopathy classification (MobilenetV2-RC), which incorporates Spatial and Channel Reconstruction Convolution (SCConv) and the improved Convolutional Block Attention Module (CBAM) attention mechanism into the framework. Not only can it effectively limit feature redundancy to reduce model parameters, but also enhance the ability of feature representation to improve classification accuracy. The parameters of the proposed model are only 1.96 M with an overall accuracy of 98.96%, which is higher 3.32% than the original MobilenetV2. Compared with ResNet18, InceptionV3, and VGG16_BN, the overall accuracy is increased by 4.6%, 6.3%, and 3.9%, respectively. The test results of UCSD and Duke open-source datasets are more remarkable. Experimental results show that our proposed algorithm has strong reliability and generalization for the accurate classification of retinopathy, and a greater application prospect in the intelligent diagnosis of ophthalmology and mobile detection terminals.

Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images

Glaucoma, a leading cause of permanent blindness worldwide, necessitates early detection to prevent vision loss, a task that is challenging and time-consuming when performed manually. This study proposes an automatic glaucoma detection method on enhanced retinal images using deep learning. The system analyzes retinal images, generating masks for the optic disc and optic cup, and providing a classification for glaucoma diagnosis. We employ a U-Net architecture with a pretrained residual neural network (ResNet34) for segmentation and an EfficientNetB0 for classification. The proposed framework is tested on publicly available datasets, including ORIGA, REFUGE, RIM-ONE DL, and HRF. Our work evaluated the U-Net model with five pretrained backbones (ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0) and examined preprocessing effects. We optimized model training with limited data using transfer learning and data augmentation techniques. The segmentation model achieves a mean intersection over union (mIoU) value of 0.98. The classification model shows remarkable performance with 99.9% training and 100% testing accuracy on ORIGA, 99.9% training and 99% testing accuracy on RIM-ONE DL, and 98% training and 100% testing accuracy on HRF. The proposed model outperforms related works and demonstrates potential for accurate glaucoma classification and detection tasks.

Collaborative masking based speckle disentanglement for self-supervised optical coherence tomography image despeckling

Optical coherence tomography (OCT) has proven to be an effective and safe diagnostic tool in clinical settings due to its unique advantages. Nonetheless, the OCT images are unavoidably corrupted with speckle noise. Despeckling has become a vital preliminary step in OCT based image processing and analysis tasks. The supervised deep learning (DL) based denoising algorithms have recently attracted much attention due to their promising performance, but the lack of the noise-free OCT images renders it difficult to provide the effective supervision for DL model training. This paper has presented a self-supervised OCT image despeckling approach which does not depend on the reference clean image and combines the disentangled representation strategy with speckle noise estimation for the effective denoising. The proposed algorithm firstly disentangles the corrupted image into the speckle noise and clean image. Then, the multiple generated clean images are multiplied by the disentangled speckle noise to synthesize the speckle-corrupted image for self-supervised learning. To make the generated clean image and extracted speckle noise more statistically realistic, a collaborative masking strategy and a noise calibration module are applied to explore the spatial correlation of the clean image and refine speckle noise. The proposed method has been validated on two public datasets. Experimental results demonstrate that our method has superior speckle reduction performance to some traditional and DL-based despeckling approaches in terms of visual evaluation and quantitative metrics. Particularly, our method provides contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) improvements of at least 0.35 dB and 14.37 dB over the compared methods, respectively. Additionally, the retinal image classification results provide further evidence for the exceptional despeckling performance of our approach against other compared methods. It is obvious that the developed method has significant advantages in keeping the fidelity of retinal structure and enhancing the speckle suppression level. Meanwhile, the presented method holds certain reference value for improving diagnostic efficiency in clinical.

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

Cross-modal attention network for retinal disease classification based on multi-modal images.

Multi-modal eye disease screening improves diagnostic accuracy by providing lesion information from different sources. However, existing multi-modal automatic diagnosis methods tend to focus on the specificity of modalities and ignore the spatial correlation of images. This paper proposes a novel cross-modal retinal disease diagnosis network (CRD-Net) that digs out the relevant features from modal images aided for multiple retinal disease diagnosis. Specifically, our model introduces a cross-modal attention (CMA) module to query and adaptively pay attention to the relevant features of the lesion in the different modal images. In addition, we also propose multiple loss functions to fuse features with modality correlation and train a multi-modal retinal image classification network to achieve a more accurate diagnosis. Experimental evaluation on three publicly available datasets shows that our CRD-Net outperforms existing single-modal and multi-modal methods, demonstrating its superior performance.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) , BCNN (VGG19) , and BCNN (Inception_V3) , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.

Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models

Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer- Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed system leverages Convolutional Neural Networks (CNNs) employing pre-trained deep learning models. Through the application of fine-tuning techniques, our model is trained on fundus images of diabetic retinopathy with resolutions of 350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform, utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate the efficacy of our approach. The achieved Area Under the Curve (AUC) values for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50, 0.70, 0.53, 0.63, and 0.69, respectively.

Research on Retinal OCT Image Classification Based on Deep Learning

Presently, there persists an issue in convolutional neural network (CNN)-based classification methods for retinal optical coherence tomography (OCT) images, particularly in discerning small-scale lesion areas. This dilemma results in neural network models exhibiting diminished accuracy when determining the activity of age-related macular degeneration (AMD) regarding its dry and wet stages, as well as choroidal neovascularization (CNV). Accurate identification of lesion types is paramount for ophthalmologists in devising treatment strategies. To address this challenge, a transfer learning-based EfficientNet retinal OCT image classification algorithm is proposed. Initially, retinal OCT images undergo data augmentation and preprocessing procedures. Subsequently, the pre-trained EfficientNet-B3 model is trained via transfer learning, followed by fine-tuning training using partial oversampling and class weighting techniques. The ultimate classification accuracy reaches 99.2%, signifying the model's commendable classification recognition accuracy. This underscores the clinical guidance significance of this research endeavor.

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

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 onimages. 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.

Improving retinal OCT image classification accuracy using medical pre-training and sample replication methods

Eye disease is a global health issue involving a large population, and it is necessary to develop high-accuracy diagnostic methods. Manual diagnosis has some challenges, and deep learning for automatic diagnosis is becoming a research focus. However, there are two problems in current deep learning used for medical image tasks: deficient network pre-training on ImageNet dataset consisted of natural scene images, which significantly differ from medical images; a discrepancy between sub-network for training and full network for prediction introduced by dropout. In this study, a medical pre-training method and a sample replication method were proposed to address the problems, respectively. The former involves network pre-training on a large-scale medical image dataset called RadImageNet, followed by transfer learning on a retinal OCT (Optical Coherence Tomography) image dataset. The latter uses JS (Jenson's Shannon) divergence between the two predictions yielded with two identical samples via sample replication as an additional loss function to enforce network’s outputs consistency. The two methods were applied on widely used ResNet50, DenseNet121 and InceptionV3 networks, and the effectiveness of using each method and both the two methods to improve the network's performance was demonstrated by experimental results. Compared to those of the corresponding base networks, most the metrics of the networks using the methods were improved for all four sample categories, and the accuracy improvements of 3.76 %, 6.52 % and 8.63 % were obtained for above the networks, respectively. All the networks achieved an accuracy of about 95 % even working with limited target dataset and training resources.

Retinal OCT Image Classification Based on CNN-RNN Unified Neural Networks

<p>Computer-aided diagnosis of retinopathy is a hot research topic in the field of medical image classification, where optical coherence tomography (OCT) is an important basis for the diagnosis of ophthalmic diseases. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, two publicly available retinal OCT image datasets are integrated and screened. Then, an end-to-end deep learning algorithmic framework based on CNN-RNN Unified Neural Networks was proposed to automatically and reliably classify six categories of retinal OCT images. Numerical results suggest that the proposed algorithm works well in terms of accuracy, precision, sensitivity and specificity, approaching or even partially surpassing the performance of clinical experts. It is valuable in promoting computer-aided diagnosis towards practical clinical applications and improving the efficiency of clinical diagnosis of retinal diseases.</p> <p> </p>

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.

Multi-label classification of retinal disease via a novel vision transformer model.

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

Efforts of Neutrosophic Logic in Medical Image Processing and Analysis

Medical image processing is indispensable for correct diagnosis and planning of treatment. However, it is susceptible to many errors due to noise, artifacts, and the variability innate in anatomical structures themselves. Traditional image analysis methods hence suffer from these complexities in the images themselves and lead to probable inaccuracies in image analysis. This paper probes into the role of neutrosophic logic in the domain of medical image processing to seek better handling of these problems. The main objectives of the work were to optimize the noise reduction, image segmentation, feature extraction, and classification using the special capabilities of neutrosophic logic directed toward handling uncertainty and indeterminacy. Contributions The contributions of this study are multifaceted: it contributes by introducing detailed support for applying neutrosophic logic in a number of medical image processing tasks and integrates neutrosophic logic with prior techniques and evaluates their performance with traditional methods. The experimental results in the study are complete and demonstrate significant improvements in key metrics. For example, applying neutrosophic logic in noise reduction increased the peak signal-to-noise ratio of MRI images from 25 dB to 35 dB. In some segmentation tasks, the Dice coefficient for liver CT scans increased from 0.85 to 0.92. It increases the accuracy of feature extraction in breast cancer detection from 88% to 95%, while integrating neutrosophic logic with convolutional neural networks improves the accuracy in retinal image classification from 92% to 97%. All these results underline the strong role that neutrosophic logic can play in enhancing accuracy, robustness, and reliability in the processing of medical images. The result of the study concludes that neutrosophic logic not only improves the current limitations but also holds great promise for handling uncertainty in many medical fields, opening a promising way for future advancements in the field of medical imaging and health applications.

Screening Retinal Images and Extraction of the Retinal Blood Vessel for Identifying Diseases and Classification of Arteries and Veins by Using Deep Learning

Abstract: In recent years, the extraction of retinal blood vessels from low contrast retinal images has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma. Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.

Retinal fundus image classification for diabetic retinopathy using transfer learning technique

Retinal fundus image classification for diabetic retinopathy using transfer learning technique

A refined ResNet18 architecture with Swish activation function for Diabetic Retinopathy classification

Automatic detection of Diabetic Retinopathy (DR) is critically important, as it is the primary reason of irreversible loss of vision in the economically active populations in the developed countries. Early detection of the onset of Diabetic Retinopathy can greatly benefit clinical treatment; although several different feature extraction methods have been proposed, the task of retinal image classification remains tedious even for trained clinicians. This paper emphasizes on Diabetic Retinopathy detection as well as the analysis of the different stages of DR, performed on fundus images using Deep Learning algorithms. Fundus images of the patient were provided as input to the developed model evaluated using the real-time dataset of the hospital. The proposed ResNet-18 architecture with swish function has achieved an accuracy of 93.51%, sensitivity of 93.42%, precision of 93.77% and F1-score of 93.59%. The paper concludes with a comparative study of Simple CNN, VGGNet-16, MobileNet-V2 and ResNet architectures and other state-of-art approaches, which highlights ResNet-18 with Swish as the most effective deep learning classifier model for DR detection.

Generative Adversarial Networks in Retinal Image Classification

The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue holds significant importance, as images play a vital role in various domains, including image recognition and bioimaging classification in the biomedical field. In this paper, we present a method to assess the distinguishability of bioimages generated by a generative adversarial network, specifically using a dataset of retina images. Once the images are generated, we train several supervised machine learning models to determine whether these classifiers can effectively discriminate between real and fake retina images. Our experiments utilize a deep convolutional generative adversarial network, a type of generative adversarial network, and demonstrate that the generated images, although visually imperceptible as fakes, are correctly identified by a classifier with an F-Measure greater than 0.95. While the majority of the generated images are accurately recognized as fake, a few of them are not classified as such and are consequently considered real retina images.