Automated Diabetic Retinopathy Detection Using Bag of Words Approach

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Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
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"Automated Diabetic Retinopathy Detection Using Bag of Words Approach"
written by Monzurul Islam, Anh V. Dinh, Khan A. Wahid,
published by Journal of Biomedical Science and Engineering, Vol.10 No.5B, 2017

has been cited by the following article(s):

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[2]	Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning-A Review
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[3]	Neural network training method for classifications of diabetic retinopathy image data on matlab
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[4]	A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features. Diagnostics 2023, 13, 1816
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[5]	Advancing Diabetic Retinopathy Detection: Leveraging Deep Learning for Accurate Classification and Early Diagnosis
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[10]	Robust Approach for Detecting Haemorrhage and Microaneurysyms from Fundus Images
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[11]	Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models
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[12]	A hybrid technique for Diabetic Retinopathy Detection based on ensemble-optimized CNN and texture features
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[13]	DETEKSI OPTIK DISK PADA CITRA RETINA MENGGUNAKAN FASTER RCNN DENGAN BACKBONE RESNTE-50
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[14]	Analysis of deep learning methods in diabetic retinopathy disease identification based on retinal fundus image
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[15]	Fast and robust exudate detection in retinal fundus images using extreme learning machine autoencoders and modified kaze features
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[16]	The role of different retinal imaging modalities in predicting progression of diabetic retinopathy: A survey
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[17]	The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey
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[18]	An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images
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[19]	Classification of fundus images based on severity utilizing SURF features from the enhanced green and value planes
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[21]	Automated Detection of fundus retinal image using EGODD algorithm
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[22]	DIABETIC RETINOPATHY CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK AND STACKING ENSEMBLE OF CLASSIFIERS TECHNIQUES
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[23]	Classification of Diabetic Retinopathy Disease with Transfer Learning using Deep Convolutional Neural Networks.
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[26]	Comparative Evaluation of Machine Learning Techniques for Detection of Diabetic Retinopathy.
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[27]	A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification
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[28]	Detection of Severity Level of Diabetic Retinopathy in Retinal Fundus Images by Rule Based Fusion Method using Deep Learning Approach
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[37]	Görüntü işleme ve evrişimsel sinir ağları kullanılarak diyabetik retinopati hastalığının tespiti
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[39]	Intelligent System for Screening Diabetic Retinopathy by Using Neutrosophic and Statistical Fundus Image Features.
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[40]	Techniques for Exudate Detection for Diabetic Retinopathy
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[41]	DETEKSI EKSUDAT PADA RETINA DENGAN PENYAKIT DIABETES SECARA SEGMENTASI MENGGUNAKAN ADAPTIVE THRESHOLDING
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[44]	Pendeteksian Eksudat pada Retina dengan fungsi SURF Sebagai Salah Satu Ciri untuk Mendiagnosa Diabetik Retinopati
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[46]	Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer
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[47]	Detection of diabetic retinopathy using computational model of human visual system
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[48]	Detection of diabetic retinopathy using computational model of human visual system.
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[49]	Diagnogsis of Diabetic Retinopathy Using Image Processing and Convolutional Neural Network
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[50]	Diagnosis of Diabetic Retinopathy by Using Image Processing and Convolutional Neural Network
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[51]	Identification and Classification of Diabetic Retinopathy Using Machine Learning
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[52]	Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer Bag of deep features for preoperative prediction of sentinel …
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[53]	Retinopathy Disease Extraction exhausting Digital Image Processing Techniques
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[54]	Diabetic Retinopathy Disease Extraction using Digital Image Processing Techniques–A Review
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[55]	DETEKSI OPTIK DISK PADA CITRA RETINA MENGGUNAKAN FASTER-RCNN DENGAN BACKBONE RESNET-50

[1]	Automated diabetic retinopathy screening using deep learning Multimedia Tools and Applications, 2024 DOI:10.1007/s11042-024-18149-4

[2]	Automated diabetic retinopathy screening using deep learning Multimedia Tools and Applications, 2024 DOI:10.1007/s11042-024-18149-4

[3]	HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading Biomedical Technology, 2024 DOI:10.1016/j.bmt.2024.09.004

[4]	Automated microaneurysms detection in retinal images using SSA optimised U-NET and Bayesian optimised CNN Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023 DOI:10.1080/21681163.2023.2244603

[5]	Automated microaneurysms detection in retinal images using SSA optimised U-NET and Bayesian optimised CNN Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023 DOI:10.1080/21681163.2023.2244603

[6]	Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models 2023 Seventh International Conference on Image Information Processing (ICIIP), 2023 DOI:10.1109/ICIIP61524.2023.10537711

[7]	An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15135-0

[8]	Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15045-1

[9]	Machine Learning and Computational Intelligence Techniques for Data Engineering Lecture Notes in Electrical Engineering, 2023 DOI:10.1007/978-981-99-0047-3_72

[10]	A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features Diagnostics, 2023 DOI:10.3390/diagnostics13101816

[11]	Robust Approach for Detecting Haemorrhage and Microaneurysyms from Fundus Images 2023 4th International Conference for Emerging Technology (INCET), 2023 DOI:10.1109/INCET57972.2023.10170467

[12]	Advancing Diabetic Retinopathy Detection: Leveraging Deep Learning for Accurate Classification and Early Diagnosis 2023 8th International Conference on Communication and Electronics Systems (ICCES), 2023 DOI:10.1109/ICCES57224.2023.10192868

[13]	Automated microaneurysms detection in retinal images using SSA optimised U-NET and Bayesian optimised CNN Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023 DOI:10.1080/21681163.2023.2244603

[14]	An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15135-0

[15]	Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15045-1

[16]	The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey Sensors, 2022 DOI:10.3390/s22093490

[17]	An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images Multimedia Tools and Applications, 2022 DOI:10.1007/s11042-022-12492-0

[18]	Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features Journal of Digital Imaging, 2022 DOI:10.1007/s10278-022-00587-x

[19]	The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey Bioengineering, 2022 DOI:10.3390/bioengineering9080366

[20]	The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey Bioengineering, 2022 DOI:10.3390/bioengineering9080366

[21]	The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey Sensors, 2022 DOI:10.3390/s22093490

[22]	Deep Learning for Medical Decision Support Systems Studies in Computational Intelligence, 2021 DOI:10.1007/978-981-15-6325-6_4

[23]	Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2021 DOI:10.1007/s40998-020-00367-9

[24]	Classification of Diabetic Retinopathy disease with Transfer Learning using Deep Convolutional Neural Networks Advances in Electrical and Computer Engineering, 2021 DOI:10.4316/AECE.2021.03006

[25]	Design of Density Clustering In Diabetic Retinopathy Based Eye Fundus Segmentation IOP Conference Series: Materials Science and Engineering, 2021 DOI:10.1088/1757-899X/1084/1/012006

[26]	Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms IEEE Access, 2020 DOI:10.1109/ACCESS.2020.2993937

[27]	Soft Computing for Problem Solving Advances in Intelligent Systems and Computing, 2020 DOI:10.1007/978-981-15-0184-5_14

[28]	RETRACTED ARTICLE: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network Neural Computing and Applications, 2020 DOI:10.1007/s00521-018-03974-0

[29]	Techniques for Exudate Detection for Diabetic Retinopathy 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2019 DOI:10.1109/ICIMCIS48181.2019.8985226

[30]	An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network Neural Computing and Applications, 2019 DOI:10.1007/s00521-018-03974-0

[31]	Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features Journal of Medical Systems, 2019 DOI:10.1007/s10916-019-1222-8

[32]	Intelligent Data Analysis for Biomedical Applications 2019 DOI:10.1016/B978-0-12-815553-0.00007-0

[33]	Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer Physics in Medicine & Biology, 2018 DOI:10.1088/1361-6560/aaf241

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