Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues
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Title
Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues
Authors
Keywords
Diabetic retinopathy, Convolutional neural network, DIARETDB1, Image preprocessing, Artificial neural network, Transfer learning
Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Nature
Online
2019-01-05
DOI
10.1007/s11042-018-7044-8
References
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Note: Only part of the references are listed.- A review on exudates detection methods for diabetic retinopathy
- (2018) Shilpa Joshi et al. BIOMEDICINE & PHARMACOTHERAPY
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- (2018) Xiao Chen et al. MULTIMEDIA TOOLS AND APPLICATIONS
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- (2018) Haiping Yu et al. MULTIMEDIA TOOLS AND APPLICATIONS
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- (2017) Su Wang et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
- (2017) Jen Hong Tan et al. INFORMATION SCIENCES
- Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients
- (2017) Rafia Mumtaz et al. International Journal of Diabetes in Developing Countries
- Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes
- (2017) Daniel Shu Wei Ting et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Hybrid classifier and region-dependent integrated features for detection of diabetic retinopathy
- (2017) V.M. Mane et al. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
- Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion
- (2017) Buket D. Barkana et al. KNOWLEDGE-BASED SYSTEMS
- Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features
- (2017) Qaisar Abbas et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Deep image mining for diabetic retinopathy screening
- (2017) Gwenolé Quellec et al. MEDICAL IMAGE ANALYSIS
- Automated Identification of Diabetic Retinopathy Using Deep Learning
- (2017) Rishab Gargeya et al. OPHTHALMOLOGY
- Multi-level deep supervised networks for retinal vessel segmentation
- (2017) Juan Mo et al. International Journal of Computer Assisted Radiology and Surgery
- Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method
- (2017) Wei Zhou et al. IEEE Access
- Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy
- (2017) Hidenori Takahashi et al. PLoS One
- Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images
- (2016) D. Santhi et al. Biomedical Engineering-Biomedizinische Technik
- Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion
- (2016) Pavle Prentašić et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images
- (2016) Mark J. J. P. van Grinsven et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Diabetic Retinopathy Diagnosis in Retinal Images Using Hopfield Neural Network
- (2016) D. Jude Hemanth et al. IETE JOURNAL OF RESEARCH
- Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening
- (2016) Tien Yin Wong et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
- (2016) Varun Gulshan et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images
- (2016) D. Santhi et al. Biomedical Engineering-Biomedizinische Technik
- Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms
- (2015) G. Mahendran et al. COMPUTERS & ELECTRICAL ENGINEERING
- Referral system for hard exudates in eye fundus
- (2015) Syed Ali Gohar Naqvi et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Retinal vessel extraction using Lattice Neural Networks with dendritic processing
- (2015) Roberto Vega et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System
- (2015) T. Jaya et al. JOURNAL OF DIGITAL IMAGING
- Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening
- (2015) Sarni Suhaila Rahim et al. NEURAL COMPUTING & APPLICATIONS
- Multi-retinal disease classification by reduced deep learning features
- (2015) R. Arunkumar et al. NEURAL COMPUTING & APPLICATIONS
- Hierarchical retinal blood vessel segmentation based on feature and ensemble learning
- (2015) Shuangling Wang et al. NEUROCOMPUTING
- Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images
- (2014) S. Wilfred Franklin et al. Biocybernetics and Biomedical Engineering
- A SEQUENTIAL LEARNING METHOD FOR DETECTION AND CLASSIFICATION OF EXUDATES IN RETINAL IMAGES TO ASSESS DIABETIC RETINOPATHY
- (2014) M. PONNIBALA et al. JOURNAL OF BIOLOGICAL SYSTEMS
- An ensemble-based system for automatic screening of diabetic retinopathy
- (2014) Bálint Antal et al. KNOWLEDGE-BASED SYSTEMS
- Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images
- (2014) Karthikeyan Ganesan et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Genetics in Diabetic Retinopathy: Current Concepts and New Insights
- (2013) Olga Simó-Servat et al. CURRENT GENOMICS
- Comparison between supervised and unsupervised classifications of neuronal cell types: A case study
- (2010) Luis Guerra et al. Developmental Neurobiology
- Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa
- (2009) Guanmin Chen et al. BMC Medical Research Methodology
- A systematic analysis of performance measures for classification tasks
- (2009) Marina Sokolova et al. INFORMATION PROCESSING & MANAGEMENT
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