DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.

Convolutional Neural Network-Based Classificationof Multiple Retinal Diseases Using Fundus Images

Use of deep learning algorithms for the investigation and analysis of medical images has emerged as a powerful technique.The increase in retinal dis-eases is alarming as it may lead to permanent blindness if left untreated.Automa-tion of the diagnosis process of retinal diseases not only assists ophthalmologists in correct decision-making but saves time also.Several researchers have worked on automated retinal disease classification but restricted either to hand-crafted fea-ture selection or binary classification.This paper presents a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images.For this research,the data has been collected and combined from three distinct sources.The images are preprocessed for enhancing the details.Six layers of the convolutional neural network(CNN)are used for the automated feature extraction and classification of 20 retinal diseases.It is observed that the results are reliant on the number of classes.For binary classification(healthy vs.unhealthy),up to 100%accuracy has been achieved.When 16 classes are used(treating stages of a disease as a single class),93.3%accuracy,92%sensitivity and 93%specificity have been obtained respectively.For 20 classes(treating stages of the disease as separate classes),the accuracy,sensitivity and specificity have dropped to 92.4%,92%and 92%respectively.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid blindness.Retinal Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye illnesses.Exudates can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic retinopathy.With that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level classifications.The model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst level.The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of classification.Exudate detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable results.Furthermore,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose DR.Furthermore,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

A novel method for detection of hard exudates from fundus images based on SVM and improved FCM

Diabetic retinopathy(DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates(EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM(IFCM) as well as support vector machines(SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65% and a mean positive predictive value of 97.25%. With an image-based criterion, our approach reached a 100% mean sensitivity, 96.43% mean specificity and 98.21% mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.

Automated Detection of Optic Disc from Digital Retinal Fundus Images for Screening Systems of Diabetic Retinopathy

Optic disc(OD)detection is a main step while developing automated screening systems for diabetic retinopathy.We present a method to automatically locate and extract the OD in digital retinal fundus images.Based on the property that main blood vessels gather in OD,the method starts with Otsu thresholding segmentation to obtain candidate regions of OD.Consequently,the main blood vessels which are segmented in H channel of color fundus images in Hue saturation value(HSV)space.Finally,a weighted vessels’direction matched filter is proposed to roughly match the direction of the main blood vessels to get the OD center which is used to pick the true OD out from the candidate regions of OD.The proposed method was evaluated on a dataset containing 100 fundus images of both normal and diseased retinas and the accuracy reaches 98%.Furthermore,the average time cost in processing an image is 1.3 s.Results suggest that the approach is reliable,and can efficiently detect OD from fundus images.

A narrative review of glaucoma screening from fundus images

The objective of the paper is to provide a general view for automatic cup to disc ratio(CDR)assessment in fundus images.As for the cause of blindness,glaucoma ranks as the second in ocular diseases.Vision loss caused by glaucoma cannot be reversed,but the loss may be avoided if screened in the early stage of glaucoma.Thus,early screening of glaucoma is very requisite to preserve vision and maintain quality of life.Optic nerve head(ONH)assessment is a useful and practical technique among current glaucoma screening methods.Vertical CDR as one of the clinical indicators for ONH assessment,has been well-used by clinicians and professionals for the analysis and diagnosis of glaucoma.The key for automatic calculation of vertical CDR in fundus images is the segmentation of optic cup(OC)and optic disc(OD).We take a brief description of methodologies about the OC and disc optic segmentation and comprehensively presented these methods as two aspects:hand-craft feature and deep learning feature.Sliding window regression,super-pixel level,image reconstruction,super-pixel level low-rank representation(LRR),deep learning methodologies for segmentation of OD and OC have been shown.It is hoped that this paper can provide guidance and bring inspiration to other researchers.Every mentioned method has its advantages and limitations.Appropriate method should be selected or explored according to the actual situation.For automatic glaucoma screening,CDR is just the reflection for a small part of the disc,while utilizing comprehensive factors or multimodal images is the promising future direction to furthermore enhance the performance.

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time.Automatic cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye treatment.In recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been exponential.Due to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field challenging.To address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this research.The study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient records.The significant features are extracted and normalized using the min-max normalization technique.In the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two clients.Before transferring the attributes to the global model,the suggested method trains the local model.The global model subsequently improves the technique after integrating the new parameters.Every client analyses the results in three rounds to decrease the over-fitting problem.The experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s data.The experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

Air tamponade in retinal detachment surgery followed by ultra-widefield fundus imaging system

AIM： To report the surgical result of pars plana vitrectomy（PPV） with air tamponade for rhegmatogenous retinal detachment（RRD） by ultra-widefield fundus imaging system. METHODS： Of 25 consecutive patients（25 eyes） with fresh primary RRD and causative retinal break and vitreous traction were presented. All the patients underwent PPV with air tamponade. Visual acuity（VA） was examined postoperatively and images were captured by ultrawidefield scanning laser ophthalmoscope system（Optos）. RESULTS： Initial reattachment was achieved in 25 cases（100%）. The air volume was 〉60% on the postoperative day（POD） 1. The ultra-widefield images showed that the retina was reattached in all air-filled eyes postoperatively. The retinal break and laser burns in the superior were detected in 22 of 25 eyes（88%）. A missed retinal hole was found under intravitreal air bubble in 1 case（4%）. The air volume was range from 40% to 60% on POD 3. A doublelayered image was seen in 25 of 25 eyes with intravitreal gas. Retinal breaks and laser burns around were seen in the intravitreal air. On POD 7, small bubble without effect was seen in 6 cases（24%） and bubble was completely disappeared in 4 cases（16%）. Small oval bubble in the superior area was observed in 15 cases（60%）. There were no missed and new retinal breaks and no retinal detachment in all cases on the POD 14 and 1 mo and last follow-up. Air disappeared completely on a mean of 9.84 d postoperatively. The mean final postoperative bestcorrected visual acuity（BCVA） was 0.35 log MAR. Mean final postoperative BCVA improved significantly relative to mean preoperative（P〈0.05）. Final VA of 0.3 log MAR or better was seen in 13 eyes. CONCLUSION： PPV with air tamponade is an effective management for fresh RRD with superior retinal breaks. The ultra-widefield fundus imaging can detect postoperative retinal breaks in air-filled eyes. It would be a useful facility for follow-up after PPV with air tamponade. Facedown position and acquired visual rehabilitation may be shorten.

Adaptive optics scanning laser ophthalmoscopy in fundus imaging, a review and update

Adaptive optics scanning laser ophthalmoscopy（AOSLO） has been a promising technique in funds imaging with growing popularity. This review firstly gives a brief history of adaptive optics（AO） and AO-SLO. Then it compares AO-SLO with conventional imaging methods（fundus fluorescein angiography, fundus autofluorescence, indocyanine green angiography and optical coherence tomography） and other AO techniques（adaptive optics flood-illumination ophthalmoscopy and adaptive optics optical coherence tomography）. Furthermore, an update of current research situation in AO-SLO is made based on different fundus structures as photoreceptors（cones and rods）, fundus vessels, retinal pigment epithelium layer, retinal nerve fiber layer, ganglion cell layer and lamina cribrosa. Finally, this review indicates possible research directions of AO-SLO in future.

Automated Exudates Detection in Retinal Fundus Image Using Morphological Operator and Entropy Maximization Thresholding

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

The application of ultra-wide-field fundus autofluorescence in early metastatic choroidal tumor screening

I am Dr.Ke Yao,from Eye Center,the Second Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou,China.I write to present three cases with metastatic choroidal tumor using an ultra-wide-field scanning laser ophthalmoscope.

Wide-Field Vibrational Phase Contrast Imaging Based on Coherent Anti-Stokes Raman Scattering Holography

We propose and implement a wide-field vibrational phase contrast detection to obtain imaging of imaginary components of third-order nonlinear susceptibility in a coherent anti-Stokes Raman scattering （CARS） microscope with full suppression of the non-resonant background. This technique is based on the unique ability of recovering the phase of the generated CARS signal based on holographic recording. By capturing the phase distributions of the generated CARS field from the sample and from the environment under resonant illumination, we demonstrate the retrieval of imaginary components in the CARS microscope and achieve background free coherent Raman imaging.

Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma.Glaucoma is an incurable and unavoidable eye disease that damages the vision ofoptic nerves and quality of life. Classification of Glaucoma has been an active fieldof research for the past ten years. Several approaches for Glaucoma classification areestablished, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). Incontrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasetsdetermines the performance of the CNN;the model trained with small datasets,overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current workcompares with other models, namely VGG16, InceptionV3, and Xception usingpublic datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, andACRIMA. The dataset has split into training, validation, and testing with the ratioof 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listedabove. The proposed method achieved 99.38% accuracy and also better results forother metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, andArea Under Curve (AUC) compared to other models.

Using Genetic Algorithm for Identification of Diabetic Retinal Exudates in Digital Color Images

Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathology on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. In this paper, a new hybrid approach called the (Genetic algorithm and vertex chain code) for blood vessel detection. And this method uses geometrical parameters of retinal vascular tree for diagnosing of hypertension and identified retinal exudates automatically from color retinal images. The skeletons of the segmented trees are produced by thinning. Three types of landmarks in the skeleton must be detected: terminal points, bifurcation and crossing points, these points are labeled and stored as a chain code. Results of the proposed system can achieve a diagnostic accuracy with 96.0% sensitivity and 98.4% specificity for the identification of images containing any evidence of retinopathy.

基于条带池化与血管增强的眼底图像动静脉分类方法

视网膜血管动静脉管径比是定量分析糖尿病、高血压等慢性疾病的先决条件,是许多心血管疾病的重要风险指标。随着深度学习技术的发展,许多基于卷积神经网络的方法凭借其捕获高级语义的能力,在眼底图像动静脉分类方面取得了较大的进展。然而,这些方法大多是采用叠加局部卷积和池化操作方式,难以很好地应用于条带形状的眼底视网膜血管。在本研究中,为了更有效地提取条带形状的视网膜血管特征,引入条带池化来捕获空间像素远距离依赖关系,同时考虑到动静脉交错复杂的特性,结合空间金字塔池化并提出了一种全新的混合池化技术以扩大神经网络的感受野和学习上下文信息。另一方面,考虑到眼底图像中血管与非血管分布的比例极不平衡,引入了血管增强模块,利用血管分布信息和高斯核函数约束的血管边缘的信息作为权重校正动静脉特征抑制背景特征,进而解决血管与背景分布比例不平衡问题。在分别包含40、22、45张彩色眼底图像的3种国际公开数据集DRIVE、LES和HRF上的实验表明,所提算法的平衡精度(BACC)分别为0.955、0.946、0.967,表明本研究结合条带池化与血管增强的方法能够较好解决眼底图像中动静脉交错复杂和类别不平衡问题,实现对眼底视网膜动静脉的精确分类,具有较高的应用价值。

以疾病为中心的眼底多模影像在专业学位硕士研究生教学中的应用

眼底病学是眼科最复杂的领域之一,也是眼科学习的重点和难点。眼底影像是辅助眼底病诊疗、教学和学习的必要方式及可靠途径。眼底影像的发展已经进入多模态时代。近年来,眼底多模影像已达到不同影像多位一体,并可实现不同影像间点对点精准对位;不但是平面像与立体像的交互融合,更有结构像和功能像的有机结合。多模影像辅助眼底病教学,更有助于全方位深刻理解、系统掌握眼底病。本文针对当前眼底病教学中存在的问题,结合眼底病教学的实际需求,提出以眼底疾病为中心,以病理机制为基础,以眼底多模影像循证解读为主要教学手段,使学生高效掌握眼底病;同时,本研究将与时俱进,进一步补充完善眼科教学方法,采用发病基础与影像表现互解互证的引导式教学模式,激发学生自主学习热情、提高教学效率,并为提升学生逻辑辨证的临床思维能力提供帮助。