Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require special...Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.展开更多
Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For...Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.展开更多
Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address...Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regres-sive Segmentation based Radial Basis Image Classifier(MPCNKFTRS-RBIC)Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time.In MPCNKFTRS-RBIC Model,the ret-inal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuanfilter.Then,preprocessed retinal fundus is given for hidden layer 2 for extracting the features like color,intensity,texture with higher accuracy.After extracting these features,the Tobit Regressive Segmenta-tion process is performed by hidden layer 3 for partitioning preprocessed image within more segments by analyzing the pixel with the extracted features of the fundus image.Then,the segmented image was given to output layer.The radial basis function analyzes the testing image region of a particular class as well as training image region with higher accuracy and minimum time consumption.Simulation is performed with retinal fundus image dataset with various perfor-mance metrics namely peak signal-to-noise ratio,accuracy and time,error rate concerning several retina fundus image and image size.展开更多
Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood v...Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.展开更多
Glaucoma is the first leading cause of irreversible blindness worldwide with increasing importance in public health.Indicators of glaucoma care quality as well as efficiency would benefit public health assessments,but...Glaucoma is the first leading cause of irreversible blindness worldwide with increasing importance in public health.Indicators of glaucoma care quality as well as efficiency would benefit public health assessments,but are lacking.We propose three such indicators.First,the glaucoma coverage rate(GCR),which is the number of people known to have glaucoma divided by the total number of people with glaucoma as estimated from population-based studies multiplied by 100%.Second,the glaucoma detection rate(GDR),which is number of newly diagnosed glaucoma patients in one year divided by the population in a defined area in millions.Third,the glaucoma follow-up adherence rate(GFAR),calculated as the number of patients with glaucoma who visit eye care provider(s)at least once a year over the total number of patients with glaucoma in given eye care provider(s)in a specific period.Regularly tracking and reporting these three indicators may help to improve the healthcare system performance at national or regional levels.展开更多
基金supported in part by the Beijing Natural Science Foundation(No.4212015)China Ministry of Education-China Mobile Scientific Research Foundation(No.MCM20200102).
文摘Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.
文摘Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.
文摘Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regres-sive Segmentation based Radial Basis Image Classifier(MPCNKFTRS-RBIC)Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time.In MPCNKFTRS-RBIC Model,the ret-inal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuanfilter.Then,preprocessed retinal fundus is given for hidden layer 2 for extracting the features like color,intensity,texture with higher accuracy.After extracting these features,the Tobit Regressive Segmenta-tion process is performed by hidden layer 3 for partitioning preprocessed image within more segments by analyzing the pixel with the extracted features of the fundus image.Then,the segmented image was given to output layer.The radial basis function analyzes the testing image region of a particular class as well as training image region with higher accuracy and minimum time consumption.Simulation is performed with retinal fundus image dataset with various perfor-mance metrics namely peak signal-to-noise ratio,accuracy and time,error rate concerning several retina fundus image and image size.
文摘Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.
基金funded by Wenzhou Medical University R&D Fund,No.QTJ13009Health Innovation Talents in Zhejiang Province(2016).No.25.
文摘Glaucoma is the first leading cause of irreversible blindness worldwide with increasing importance in public health.Indicators of glaucoma care quality as well as efficiency would benefit public health assessments,but are lacking.We propose three such indicators.First,the glaucoma coverage rate(GCR),which is the number of people known to have glaucoma divided by the total number of people with glaucoma as estimated from population-based studies multiplied by 100%.Second,the glaucoma detection rate(GDR),which is number of newly diagnosed glaucoma patients in one year divided by the population in a defined area in millions.Third,the glaucoma follow-up adherence rate(GFAR),calculated as the number of patients with glaucoma who visit eye care provider(s)at least once a year over the total number of patients with glaucoma in given eye care provider(s)in a specific period.Regularly tracking and reporting these three indicators may help to improve the healthcare system performance at national or regional levels.
