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

High Technology Letters 2023年第4期29卷 377-387页

作者：蒋杰伟 ZHANG Yi XIE He GONG Jiamin ZHU Shaomin WU Shanjun LI Zhongwen School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121P.R.China School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325000P.R.China Ningbo Eye Hospital Wenzhou Medical UniversityNingbo 315000P.R.China

Cataract is the leading cause of visual impairment *** scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the *** 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 ***,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus *** 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 *** 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 *** 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.

关键词： deep learning convolutional neural network(CNN) visual impairment grading fundus image efficient channel attention

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Supervised Vessels Classification Based on Feature Selection

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Journal of Computer Science & Technology 2017年第6期32卷 1222-1230页

作者： Bei-Ji Zou Yao Chen Cheng-Zhang Zhu Zai-Liang Chen Zi-Qian Zhang School of Information Science and Engineering Central South University Changsha 410083 China ‘Mobile Health' Ministry of Education-China Mobile Joint Laboratory Central South University Changsha 410083 China College of Literature and Journalism Central South University Changsha 410083 China

Arterial-venous classification of retinal blood vessels is important for the automatic detection of cardiovascular diseases such as hypertensive retinopathy and stroke. In this paper, we propose an arterial-venous classification （AVC） method, which focuses on feature extraction and selection from vessel centerline pixels. The vessel centerline is extracted after the preprocessing of vessel segmentation and optic disc （OD） localization. Then, a region of interest （ROI） is extracted around OD, and the most efficient features of each centerline pixel in ROI are selected from the local features, grey-level co-occurrence matrix （GLCM） features, and an adaptive local binary patten （A-LBP） feature by using a max-relevance and min-redundancy （mRMR） scheme. Finally, a feature-weighted K-nearest neighbor （FW-KNN） algorithm is used to classify the arterial-venous vessels. The experimental results on the DRIVE database and INSPIRE-AVR database achieve the high accuracy of 88.65% and 88.51% in ROI, respectively.

关键词： fundus image arterial-venous classification adaptive local binary patten(A-LBP) feature selection feature-weighted K-nearest neighbor(FW-KNN)

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An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1429-1457页

作者： Hela Elmannai Monia Hamdi Souham Meshoul Amel Ali Alhussan Manel Ayadi Amel Ksibi Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information System College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of *** ranks as the second most prevalent cause of permanent *** glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait *** automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc *** methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted *** the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease *** approach presented in this study is based on deep classifiers for OD segmentation and glaucoma ***,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy *** OD region is then fed into the second stage for glaucoma ***,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc *** this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and *** also used the Support Vector Machine *** feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor *** final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is *** metrics including accuracy and ROC curve are compared for each classification *** novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global ***,the fusion-based decision system uses the g

关键词： Optic disc glaucoma fundus image deep learning

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Localization of Optic Disc in fundus images Based on Yolov3

Localization of Optic Disc in Fundus Images Based on Yolov3

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第40届中国控制会议

作者： Tianci Jiang Zhiwei Zhou Jun Yao School of Mechanical and Electrical Engineering and Automation Shanghai University

In order to improve the detection accuracy and detection speed of optic disc in fundus images,an endto-end localization method based on Yolov3 framework was proposed in this *** pre-processed fundus images were input into Yolov3 framework to locate optic discs *** proposed method achieved 100% accuracy on MESSIDOR,DIARETDBO,DIARETDB1 and DRIVE datasets.97.5% and 98% success rates were obtained on STARE and KAGGLE datasets *** average localization time was *** test results demonstrate that this proposed method can locate optic disc in both normal fundus images and the fundus images with pathological retinas robustly and rapidly.

关键词： fundus image Optic Disc Localization Yolov3

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Classification of Glaucoma in Retinal images Using EfficientnetB4 Deep Learning Model

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Computer Systems Science & Engineering 2022年第12期43卷 1041-1055页

作者： A.Geetha N.B.Prakash National Engineering College Kovilpatti628502India

Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and *** 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.

关键词： Convolution neural network deep learning fundus image glaucoma image classification

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Automated identification of retinopathy of prematurity by image-based deep learning

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Eye and Vision 2020年第4期7卷 379-390页

作者： Yan Tong Wei Lu Qin-qin Deng Changzheng Chen Yin Shen Eye Center Renmin Hospital of Wuhan UniversityWuhan 430060HubeiChina Medical Research Institute Wuhan UniversityWuhanHubeiChina

Background:Retinopathy of prematurity(ROP)is a leading cause of childhood blindness worldwide but can be a treatable retinal disease with appropriate and timely *** study was performed to develop a robust intelligent system based on deep learning to automatically classify the severity of ROP from fundus images and detect the stage of ROP and presence of plus disease to enable automated diagnosis and further ***:A total of 36,231 fundus images were labeled by 13 licensed retinal experts.A 101-layer convolutional neural network(ResNet)and a faster region-based convolutional neural network(Faster-RCNN)were trained for image classification and *** applied a 10-fold cross-validation method to train and optimize our *** accuracy,sensitivity,and specificity were assessed in a four-degree classification task to evaluate the performance of the intelligent *** performance of the system was compared with results obtained by two retinal ***,the system was designed to detect the stage of ROP and presence of plus disease as well as to highlight lesion regions based on an object detection network using ***:The system achieved an accuracy of 0.903 for the ROP severity ***,the accuracies in discriminating normal,mild,semi-urgent,and urgent were 0.883,0.900,0.957,and 0.870,respectively;the corresponding accuracies of the two experts were 0.902 and ***,our model achieved an accuracy of 0.957 for detecting the stage of ROP and 0.896 for detecting plus disease;the accuracies in discriminating stage I to stage V were 0.876,0.942,0.968,0.998 and 0.999,***:Our system was able to detect ROP and differentiate four-level classification fundus images with high accuracy and *** performance of the system was comparable to or better than that of human experts,demonstrating that this system could be used to support clinical decisions.

关键词： Deep learning Retinopathy of prematurity Artificial intelligence fundus image

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Automatic localization of macular area based on structure label transfer

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International Journal of Ophthalmology(English edition) 2018年第3期11卷 422-428页

作者： Xiao-Xin Guo Qun Li Chao Sun Yi-Nan LU Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun130012 Jilin Province China College of Computer Science and Technology Jilin University Changchun 130012 Jilin Province China

AIM： To explore feasibility and practicability of macula localization independent of macular morphological features. METHODS： A novel method was proposed to identify macula in fundus images by using structure label transfer. Its main idea was to match a processed image with the candidate images with known structures, and then transfer the structure label representing the macular to the processed image as a result of macula localization. In this way, macula localization couldn＇t be influenced by lesion or other interference any more. RESULTS： The average success rate in four datasets was 98.18%. For accuracy, the average error distance in four datasets was 0.151 optic disc diameter （ODD）. Even for severe lesion images, the proposed method can still maintain high success rate and high accuracy, e.g., 95.65% and 0.124 ODD in the case of STARE dataset, respectively, which indicated that the proposed method was highly robust and stable in the complicated situations. CONCLUSION： The proposed method can avoid the interference of lesion to macular morphological features in macula localization, and can locate macula with high accuracy and robustness, verifying its feasibility.

关键词： fundus image optic disc macula structurelabel transfer

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Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

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Computer Systems Science & Engineering 2023年第5期45卷 1985-2000页

作者： S.Sudha A.Srinivasan T.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonam612001TamilnaduIndia

The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the *** feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent ***,this type of disorder can be only screened and identified through the processing of fundus *** different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of *** the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion *** CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)*** this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

关键词： CNN networking segmentation hybrid classifier data set crossvalidation fundus image

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fundus Lesion Identification Based on The Improved FCM And Mathematical Morphology

Fundus Lesion Identification Based on The Improved FCM And M...

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2012 IEEE 3rd International Conference on Software Engineering and Service Science（ICSESS 2012）

作者： Li Hua Hui Zhang Faculty of Computer Engineering Huaiyin Institute of Technology Huai’an Jiangsu Province China

fundus lesion identification is a worldwide *** traditional method has greater impact on human factors,subjective and cumbersome makes fundus lesion identification Accuracy,objectivity and practicality not be *** solve the above problem,an improved FCM algorithm for segmentation of fundus lesion,the improved FCM algorithm clustering image segmentation fundus lesion,and then remove the noise by mathematical morphology *** results show that the algorithm can effectively identify the lesion in the fundus image.

关键词： fundus image FCM mathematical morphology lesion identification

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