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.

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.

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.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

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%).

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.

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.

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.

Multimodal imaging in acute retinal ischemia: spectral domain OCT, OCT-angiography and fundus autofluorescence

AIM： To describe retinal findings of various imaging modalities in acute retinal ischemia. METHODS： Fluorescein angiography（FA）, spectral domain optical coherence tomography（SD-OCT）, OCTangiography（OCT-A） and fundus autofluorescence（FAF） images of 13 patients（mean age 64y, range 28-86y） with acute retinal ischemia were evaluated. Six suffered from branch arterial occlusion, 2 had a central retinal artery occlusion, 2 had a combined arteriovenous occlusions, 1 patient had a retrobulbar arterial compression by an orbital haemangioma and 2 patients showed an ocular ischemic syndrome.RESULTS： All patients showed increased reflectivity and thickening of the ischemic retinal tissue. In 10 out of 13 patients SD-OCT revealed an additional highly reflective band located within or above the outer plexiform layer. Morphological characteristics were a decreasing intensity with distance from the fovea, partially segmental occurrence and manifestation limited in time. OCT-A showed a loss of flow signal in the superficial and deep capillary plexus at the affected areas. Reduced flow signal was detected underneath the regions with retinal edema. FAF showed areas of altered signal intensity at the posterior pole. The regions of decreased FAF signal corresponded to peri-venous regions. CONCLUSION： Multimodal imaging modalities in retinal ischemia yield characteristic findings and valuable diagnostic information. Conventional OCT identifies hyperreflectivity and thickening and a mid-retinal hyperreflective band is frequently observed. OCT-A examination reveals demarcation of the ischemic retinal area on the vascular level. FAF shows decreased fluorescence signal in areas of retinal edema often corresponding to peri-venous regions.

OCTA characteristics in non-arteritic central retinal artery occlusion and correlation with visual acuity

AIM:To observe the retinal and choroidal circulations in patients with non-arteritic permanent central retinal artery occlusion(NA-CRAO)via optical coherence tomography angiography(OCTA)and analyze their correlation with visual acuity.METHODS:Sixty-two eyes with clinically confirmed acute NA-CRAO were included in the study and divided into:A type(mild n=29),B type(moderate n=27)and C type(severe n=6)based on the degree of visual loss,retinal edema,and arterial blood flow delay in fundus fluorescence angiography(FFA).Contralateral healthy eyes were used as the control group.Best-corrected visual acuity(BCVA),slit lamp microscopy,indirect ophthalmoscopy,fundus color photography,OCTA,and FFA were performed.Spearman’s correlation analysis was used to determine the correlations between retinal and choroidal vessels and visual acuity.RESULTS:There were no statistically significant differences in age,gender,and intraocular pressure among the three types and the control group(P>0.05).Vessel density in deep capillary plexus(VD-DCP)significantly decreased(P<0.05)in all three types of NA-CRAO patients compared to the control group.Vessel density in superficial vascular plexus(VD-SVP)significantly decreased(P<0.05)in type A patients and choriocapillaris flow area significantly decreased(P<0.05)in type B and type C patients compared to the control group;while outer retinal flow areas significantly increased in the type A(P<0.05)and decreased in type C patients(P<0.05).The retinal thickness significantly increased in type C group(P<0.05).The VD-SVP at fovea in the type A was significantly lower than both of type B and C.The VD-SVP at nasal parafovea in type A and B was significantly lower than type C(P<0.05).The logMAR BCVA of type A was significantly better than that of type B and C groups(P<0.05).Spearman’s correlation analysis showed that the logMAR BCVA was positively correlated with VD-SVP at fovea(r=0.679,P=0.031)and nasal parafovea(r=0.826,P=0.013).CONCLUSION:OCTA is valuable for assessing retinal ischemia,and evaluating visual impairment.Deep retinal vasculature is commonly affected in all NA-CRAO types.VDSVPs at fovea and nasal parafovea can serve as reliable markers of visual impairment in NA-CRAO.

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.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

RDH12-associated retinal degeneration caused by a homozygous pathogenic variant of 146C>T and literature review

AIM:To describe the clinical,electrophysiological,and genetic features of an unusual case with an RDH12 homozygous pathogenic variant and reviewed the characteristics of the patients reported with the same variant.METHODS:The patient underwent a complete ophthalmologic examination including best-corrected visual acuity,anterior segment and dilated fundus,visual field,spectral-domain optical coherence tomography(OCT)and electroretinogram(ERG).The retinal disease panel genes were sequenced through chip capture high-throughput sequencing and Sanger sequencing was used to confirm the result.Then we reviewed the characteristics of the patients reported with the same variant.RESULTS:A 30-year male presented with severe early retinal degeneration who complained night blindness,decreased visual acuity,vitreous floaters and amaurosis fugax.The best corrected vision was 0.04 OD and 0.12 OS,respectively.The fundus photo and OCT showed bilateral macular atrophy but larger areas of macular atrophy in the left eye.Autofluorescence shows bilateral symmetrical hypo-autofluorescence.ERG revealed that the amplitudes of a-and b-wave were severely decreased.Multifocal ERG showed decreased amplitudes in the local macular area.A homozygous missense variant c.146C>T(chr14:68191267)was found.The clinical characteristics of a total of 13 patients reported with the same pathologic variant varied.CONCLUSION:An unusual patient with a homozygous pathogenic variant in the c.146C>T of RDH12 which causes late-onset and asymmetric retinal degeneration are reported.The clinical manifestations of the patient with multimodal retinal imaging and functional examinations have enriched our understanding of this disease.

Fundus photography,fluorescein angiography,optical coherence tomography and electroretinography of preclinical animal models of ocular diseases

The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).

Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion

AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models. RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P<0.001). CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.

10-year fundus tessellation progression and retinal vein occlusion

AIM： To access the 10-year fundus tessellation progression in patients with retinal vein occlusion. METHODS： The Beijing Eye Study 2001/2011 is a populationbased longitudinal study. The study participants underwent a detailed physical and ophthalmic examination. Degree of fundus tessellation was graded by using fundus photographs of the macula and optic disc. Progression of fundus tessellation was calculated by fundus tessellation degree of 2011 minus degree of 2001. Fundus photographs were used for assessment of retinal vein occlusion. RESULTS： The Beijing Eye Study included 4403 subjects in 2001, 3468 subjects was repeated in 2011. Assessment of retinal vein obstruction and fundus tessellation progression were available for 2462 subjects（71.0%）, with 66 subjects fulfilled the diagnosis of retinal vein occlusion. Of the 66 participants, 59 participants with unilateral branch retinal vein occlusion, 5 participants with unilateral central retinal vein occlusion, 1 participant with bilateral branch retinal vein occlusion, and 1 participant with branch retinal vein occlusion in one eye and central retinal vein occlusion in the other eye. Mean degree of peripapillary fundus tessellation progression were significantly higher in the whole retinal vein occlusion group（0.33±0.39, P〈0.001）, central retinal vein occlusion group（0.71±0.8, P=0.025） and branch retinal vein occlusion group（0.29±0.34, P=0.006） than the control group（0.20±0.26）. After adjustment for age, prevalence of tilted disc, change of best corrected visual acuity, axial length, progression of peripapillary fundus tessellation was associated with the presence of retinal vein occlusion（P=0.004; regression coefficient B, 0.094; 95%CI, 0.029, 0.158; standardized coefficient B, 0.056）. As a corollary, after adjusting for smoking duration, systolic blood pressure, anterior corneal curvature, prevalence of RVO was associated with more peripapillary fundus tessellation progression（P〈0.001; regression coefficient B： 1.257; OR： 3.517; 95%CI： 1.777, 6.958）. CONCLUSION： Peripapillary fundus tessellation progresses faster in individuals with retinal vein occlusion. This may reflect the thinning and hypoperfusion of choroid in patients with retinal vein occlusion.

Image Analysis for Degradation of DNA in Retinal Nuclei of Rat after Death

The changes of retinal nuclear DNA content in rats after death was detected and the relationship between degradation of retinal nuclear DNA and postmortem interval （PMI） was analyzed. Ninety healthy adult SD rats, female, weighing 250±10 g, were randomly divided into 15 groups. At 20 ℃, the retinal cells were withdrawn every 2 h within 0 to 28 h after death and stained with Feulgen-Vans. Index of density （ID）, integral absorbance （IA） and average absorbance （AA） in retinal nucleus were analyzed by image analysis system. And the obtained data were subjected to linear regression analysis by using SPSS12.0 software. The results showed that in retinal nucleus, AA and IA were gradually declined with the prolongation of PMI, while ID had an increased tendency. Within 28 h after PMI, the regression equations were as follows： YAA=-0.009XAA＋0.590 （R^2=0.949）, YIA=0.097XIA＋18.903 （R^2=0.968）, YID=0.122XID＋2.246 （R^2=0.951）. It was concluded that retinal nuclear DNA after death in rats was degraded gradually and had a good correlation with PMI.

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.

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.