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Task analysis, Training, Retinopathy, Feature extraction, Diabetes,
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convolution neural network, data augmentation,
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aggregated residual transformations, computer-aided diagnosis,
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convolution neural network, diabetic retinopathy,
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diabetic retinopathy, extreme learning machine classifier,
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fundus images, mobile health,
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Diabetic retinopathy (DR),
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diabetic retinopathy, images classification,
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2112
computer aided diagnosis, convolutional neural networks,
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2108
cross-validation, datasets, diabetic retinopathy,
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adaptive thresholding, binarization, deep belief network (DBN),
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Panhwar, S.Q.[Shahbaz Qamar],
Baqai, A.[Attiya],
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2205
artificial intelligence (AI), cystoid macular edema (CME),
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2206
Lesions, Transformers, Image segmentation, Pathology, Task analysis,
Head, Feature extraction, Diabetic retinopathy, fundus image,
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Deepalakshmi, P.[Perumalsamy],
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2207
decision tree, diabetes, expert recommendation system,
fuzzy inference system, fuzzy logic, IFIR_PDDM
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Multi-criteria optimization for optimal nutrition of Moroccan
diabetics,
ISCV22(1-6)
IEEE DOI
2208
Costs, Computational modeling, Sociology, Stochastic processes,
Linear programming, Diabetes, Task analysis,
Glycemic load
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deep learning model, neural networks, radon transform, wavelet transform
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Yu, M.[Moye],
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2209
artificial intelligence, attention mechanism, CNN,
diabetic retinopathy, dilated convolution, fundus image
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Mahmood, M.H.[Muhammad Hamdi],
Lim, L.T.[Lik Thai],
Mat, D.A.A.[Dayang Azra Awang],
Sapawi, R.[Rohana],
Sahari, S.K.[Siti Kudnie],
Lias, K.[Kasumawati],
Jali, S.K.[Suriati Khartini],
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Zhang, W.S.[Wen-Sheng],
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Robust Collaborative Learning of Patch-Level and Image-Level
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IEEE DOI
2211
Lesions, Annotations, Generators, Feature extraction,
Image segmentation, Retinopathy, Diabetes, Collaborative learning,
fundus image
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Padmapriya, M.,
Pasupathy, S.,
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Gour, M.[Mahesh],
Jain, S.[Sweta],
Kaushal, S.[Sushant],
XCapsNet: A deep neural network for automated detection of diabetic
retinopathy,
IJIST(33), No. 3, 2023, pp. 1014-1027.
DOI Link
2305
Capsule network, CLAHE, deep learning, diabetic retinopathy,
fundus image, Xception model
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Naveen, J.,
Selvam, S.[Sheba],
Selvam, B.[Blessy],
FO-DPSO Algorithm for Segmentation and Detection of Diabetic Mellitus
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IJIG(23), No. 3 2023, pp. 2240011.
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2306
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Lin, C.L.[Chun-Ling],
Jiang, Z.X.[Zhi-Xiang],
Development of preprocessing methods and revised EfficientNet for
diabetic retinopathy detection,
IJIST(33), No. 4, 2023, pp. 1450-1466.
DOI Link
2307
application programming interface (API), deep learning,
diabetic retinopathy, eye-quality library (EyeQ) EfficientNet
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Das, S.K.[Sujit Kumar],
Namasudra, S.[Suyel],
Kumar, A.[Awnish],
Moparthi, N.R.[Nageswara Rao],
AESPNet: Attention Enhanced Stacked Parallel Network to improve
automatic Diabetic Foot Ulcer identification,
IVC(138), 2023, pp. 104809.
Elsevier DOI
2310
Image Classification, Convolutional Neural Network,
Attention Module, Medical Image
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Zhang, X.F.[Xin-Feng],
Zhang, J.[JiaMing],
Zhang, Y.T.[Yi-Tian],
Jia, M.[Maoshen],
Li, H.[Hui],
Liu, X.M.[Xiao-Min],
Adaptive learning Unet-based adversarial network with CNN and
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IET-IPR(17), No. 11, 2023, pp. 3337-3348.
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convolutional neural nets, image segmentation, medical image processing
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IJIST(33), No. 6, 2023, pp. 1914-1928.
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2311
attention mechanism, diabetic retinopathy screening,
fundus image analysis, lesion segmentation, multi-task learning
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Almattar, W.[Wadha],
Luqman, H.[Hamzah],
Khan, F.A.[Fakhri Alam],
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Elsevier DOI
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Diabetic retinopathy, Retinal fundus images,
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Radha, K.,
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Retinal vessel segmentation to diagnose diabetic retinopathy using
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IJIST(34), No. 1, 2024, pp. e22945.
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2401
automatic segmentation techniques, blood vessel detection,
diabetes, diabetic retinopathy, DR detection, handcrafted segmentation
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Deer Hunting Optimization with 3D-Convolutional Neural Network for
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Macsik, P.[Peter],
Pavlovicova, J.[Jarmila],
Kajan, S.[Slavomir],
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biomedical optical imaging, computer vision,
convolutional neural nets, image classification, medical image processing
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Rubin, D.L.[Daniel L.],
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2405
Age-related macular degeneration,
Geographic atrophy segmentation, SD-OCT images,
Multitask learning
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Joseph, J.[Jovi],
Sreela, S.R.,
MODCN: Fine-Tuned Deep Convolutional Neural Network with GAN Deployed
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DOI Link
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MedImg(43), No. 8, August 2024, pp. 2960-2969.
IEEE DOI
2408
Visualization, Task analysis, Semantics, Biomedical imaging, Lesions,
Tuning, Training, Diabetic retinopathy, prompt learning,
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IVC(150), 2024, pp. 105194.
Elsevier DOI
2409
Diabetic retinopathy, Fundus disease,
Empirical wavelet transform, Neural network, Magnitude spectrum
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Andaloussi, I.B.[Idriss Benatiya],
Tairi, H.[Hamid],
Diabetic Retinopathy Prediction Based on a Hybrid Deep Learning
Approach,
ISCV24(1-5)
IEEE DOI
2408
Deep learning, Image quality, Diabetic retinopathy, Accuracy,
Manuals, Retina, Feature extraction, Image Classification,
Diabetic Retinopathy
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Galappaththige, C.J.[Chamuditha Jayanga],
Kuruppu, G.[Gayal],
Khan, M.H.[Muhammad Haris],
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WACV24(7670-7680)
IEEE DOI
2404
Deep learning, Diabetic retinopathy, Adaptation models,
Visual impairment, Predictive models, Transformers, Calibration,
and algorithms
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ElHabebe, M.[Mohamed],
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Ahmed, I.S.[Islam SH],
DR10K: Transfer Learning Using Weak Labels for Grading Diabetic
Retinopathy on DR10K Dataset,
WACV24(7733-7743)
IEEE DOI
2404
Training, Diabetic retinopathy, Telemedicine, Roads,
Transfer learning, Pipelines, Developing countries, Applications,
and algorithms
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MVA23(1-5)
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Diabetic retinopathy, Visualization, Machine vision,
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Detection Analysis of Diabetic Retinopathy Using Modified DensNet-121,
ICCVMI23(1-6)
IEEE DOI
2403
Deep learning, Diabetic retinopathy, Computational modeling,
Telemedicine, Computer architecture, Blindness, Robustness,
Data Pre-pocessing
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Panagiotou, M.[Maria],
Papathanail, I.[Ioannis],
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Vasiloglou, M.F.[Maria F.],
Stathopoulou, T.[Thomai],
de Galan, B.E.[Bastiaan E.],
Pedersen-Bjergaard, U.[Ulrik],
van der Horst, K.[Klazine],
Mougiakakou, S.[Stavroula],
A Complete AI-based System for Dietary Assessment and Personalized
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Springer DOI
2312
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Toe, T.T.[Teoh Teik],
Diabetic Retinopathy Detection Method Based on Improved Convolutional
Neural Network Using Fine-Tuning,
ICRVC22(113-117)
IEEE DOI
2301
Retinopathy, Computational modeling, Diabetes,
Convolutional neural networks, Lesions, Image enhancement, CNN, resnet34
BibRef
Chang, M.H.[Meng-Hsuan],
Chen, C.Y.[Chih-Ying],
Yu, C.H.[Chih-Han],
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Vessel Segmentation and Dirt/Reflection Detection For Retinal Fundus
Photographs,
ICIP22(3953-3957)
IEEE DOI
2211
Training, Image segmentation, Codes, Retinopathy, Retina, Generators,
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Distributional Shifts In Automated Diabetic Retinopathy Screening,
ICIP21(255-259)
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2201
Training, Retinopathy, Image processing, Detectors, Retina, Diabetes,
Distributional Shift, Dirichlet Prior Network,
Out-of-distribution
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Visual interpretability analysis of Deep CNNs using an Adaptive
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MIA-COVID19D21(480-486)
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2112
Training, Image segmentation, Visualization,
Retinopathy, Computational modeling, Transforms
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CVPR21(10933-10942)
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2111
Retinopathy, Sociology, Benchmark testing, Transformers, Decoding,
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2105
Training, Image segmentation, Retinopathy, Semantics, Lung,
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2103
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CVIDL20(585-591)
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2102
convolutional neural nets, diseases, feature extraction,
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Convolutional neural network
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Vessel-Net: A Vessel-Aware Ensemble Network For Retinopathy Screening
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ICIP20(320-324)
IEEE DOI
2011
Streaming media, Retinopathy, Biomedical imaging, Training,
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Zhao, Z.Y.[Zi-Yuan],
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Zeng, Z.[Zeng],
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Sea-Net:
Squeeze-and-Excitation Attention Net For Diabetic Retinopathy Grading,
ICIP20(2496-2500)
IEEE DOI
2011
Feature extraction, Diabetes, Retina, Computer architecture,
Retinopathy, Machine learning, Neural networks,
Attention mechanism
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BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading,
ICIP19(1385-1389)
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2007
Diabetic macular edema
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Campilho, A.,
EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks
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MVA19(1-6)
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1806
biomedical optical imaging, convolutional neural nets, diseases,
eye, feature extraction, image classification,
Predictive models
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Eladawi, N.,
Elmogy, M.,
Ghazal, M.,
Fraiwan, L.,
Aboelfetouh, A.,
Riad, A.,
Sandhu, H.,
Keynton, R.,
El-Baz, A.,
Early Signs Detection of Diabetic Retinopathy Using Optical Coherence
Tomography Angiography Scans Based on 3D Multi-Path Convolutional
Neural Network,
ICIP19(1390-1394)
IEEE DOI
1910
Early Diagnosis of DR, OCTA, Multi-path 3D CNN,
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Retina, Feature extraction, Biomedical imaging, Blood vessels,
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1807
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1807
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ICIAR18(669-678).
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ICIP17(2069-2073)
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1803
Biomedical imaging, Cams, Diabetes, Lesions, Retina, Retinopathy,
Training, deep learning, diabetic retinopathy, lesion detection,
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ICIVC17(1006-1010)
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1708
Biological system modeling, Biomedical imaging, Blood,
Computational modeling, Diabetes, Diseases,
Support vector machines, classification, data mining, diabetes, prediction
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MVA17(165-168)
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Encoding, Feature extraction, Lesions, Pathology, Retina, Retinopathy, Visualization
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ICPR16(1297-1302)
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1705
Diabetes, Feature extraction, Histograms, Pathology,
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Role of Image Contrast Enhancement Technique for Ophthalmologist as
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DICTA16(1-8)
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Biomedical imaging
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An integrated approach for Diabetic Retinopathy exudate segmentation
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ant colony optimisation
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Agurto, C.,
Chek, V.,
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A thermoregulation model to detect diabetic peripheral neuropathy,
Southwest16(13-16)
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Biological system modeling
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edge detection
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image colour analysis
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Earlier:
Classification and Localisation of Diabetic-Related Eye Disease,
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Butikova, J.,
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Texture analysis and optical anisotropy measurements of leukocytes for
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Application of Model Based Image Interpretation Methods
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9600
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Macular Degeneration Detection, AMD, Retinal Analysis Application .