Walter, T.,
Klein, J.,
Massin, P.,
Erginay, A.,
A contribution of image processing to the diagnosis of diabetic
retinopathy-detection of exudates in color fundus images of the
human retina,
MedImg(21), No. 10, October 2002, pp. 1236-1243.
IEEE Top Reference.
0301
BibRef
Jelinek, H.F.[Herbert F.],
Cree, M.J.[Michael J.],
Leandro, J.J.G.[Jorge J. G.],
Soares, J.V.B.[João V. B.],
Cesar, R.M.[Roberto M.],
Luckie, A.,
Automated segmentation of retinal blood vessels and identification of
proliferative diabetic retinopathy,
JOSA-A(24), No. 5, May 2007, pp. 1448-1456.
WWW Link.
0801
BibRef
Niemeijer, M.,
Abramoff, M.D.,
van Ginneken, B.,
Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus
Photographs,
MedImg(28), No. 5, May 2009, pp. 775-785.
IEEE DOI
0905
BibRef
Agurto, C.,
Murray, V.,
Barriga, E.,
Murillo, S.,
Pattichis, M.,
Davis, H.,
Russell, S.,
Abramoff, M.,
Soliz, P.,
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection,
MedImg(29), No. 2, February 2010, pp. 502-512.
IEEE DOI
1002
BibRef
Patil, B.M.,
Joshi, R.C.[Ramesh C.],
Toshniwal, D.[Durga],
Classification of type-2 diabetic patients by using Apriori and
predictive Apriori,
IJCVR(2), No. 3, 2011, pp. 254-265.
DOI Link
1110
BibRef
Qureshi, R.J.[Rashid Jalal],
Kovacs, L.[Laszlo],
Harangi, B.[Balazs],
Nagy, B.[Brigitta],
Peto, T.[Tunde],
Hajdu, A.[Andras],
Combining algorithms for automatic detection of optic disc and macula
in fundus images,
CVIU(116), No. 1, January 2012, pp. 138-145.
Elsevier DOI
1112
Diabetic retinopathy; Macula detection; Optic disc detection; Retinal imaging
BibRef
Ranamuka, N.G.,
Meegama, R.G.N.,
Detection of hard exudates from diabetic retinopathy images
using fuzzy logic,
IET-IPR(7), No. 2, 2013, pp. 121-130.
DOI Link
1307
BibRef
Franklin, S.W.,
Rajan, S.E.,
Diagnosis of diabetic retinopathy by employing image processing
technique to detect exudates in retinal images,
IET-IPR(8), No. 10, October 2014, pp. 601-609.
DOI Link
1411
biomedical transducers
BibRef
Magdy, E.[Eman],
Ibrahim, M.[Mohamed],
Nguyen, Q.D.[Quan D.],
Fahmy, A.S.[Ahmed S.],
Computer-aided analysis of fluorescein angiograms using colour
leakage maps,
IET-IPR(9), No. 6, 2015, pp. 486-495.
DOI Link
1507
diseases
BibRef
Magdy, E.[Eman],
Agbedia, O.O.[Owhofasa O.],
Ibrahim, M.[Mohamed],
Nguyen, Q.D.[Quan D.],
Fahmy, A.S.[Ahmed S.],
Quantitative assessment of Diabetic Macular Edema using fluorescein
leakage maps,
ICIP12(2833-2836).
IEEE DOI
1302
BibRef
Seoud, L.,
Hurtut, T.,
Chelbi, J.,
Cheriet, F.,
Langlois, J.M.P.,
Red Lesion Detection Using Dynamic Shape Features for Diabetic
Retinopathy Screening,
MedImg(35), No. 4, April 2016, pp. 1116-1126.
IEEE DOI
1604
biomedical optical imaging
BibRef
Shu, T.,
Zhang, B.,
Tang, Y.Y.,
Using K-NN with weights to detect diabetes mellitus based on genetic
algorithm feature selection,
ICWAPR16(12-17)
IEEE DOI
1611
Diabetes
BibRef
Yoon, S.Y.[Soo-Young],
Park, S.[Soonchan],
Kim, H.G.[Hyug-Gi],
Park, T.H.[Tae-Hee],
Kim, S.M.[Sun Mi],
Hwang, Y.C.[You-Cheol],
Kim, G.Y.[Gou Young],
Ryu, C.W.[Chang-Woo],
Yoon, T.Y.[Tai-Young],
Jahng, G.H.[Geon-Ho],
Quantitative susceptibility mapping in a diabetes mellitus rat model:
Iron accumulation in the brain,
IJIST(27), No. 3, 2017, pp. 238-247.
DOI Link
1708
quantitative susceptibility mapping, diabetes rat, brain, , iron, accumulation
BibRef
Vogl, W.D.,
Waldstein, S.M.,
Gerendas, B.S.,
Schmidt-Erfurth, U.,
Langs, G.,
Predicting Macular Edema Recurrence from Spatio-Temporal Signatures
in Optical Coherence Tomography Images,
MedImg(36), No. 9, September 2017, pp. 1773-1783.
IEEE DOI
1709
biomedical optical imaging, diseases, eye,
optical tomography, SD-OCT scans,
branch retinal vein occlusion,
macular edema recurrence prediction,
retinal thickness features, Veins,
BibRef
de la Torre, J.[Jordi],
Puig, D.[Domenec],
Valls, A.[Aida],
Weighted kappa loss function for multi-class classification of
ordinal data in deep learning,
PRL(105), 2018, pp. 144-154.
Elsevier DOI
1804
Deep learning, Convolutional neural networks,
Supervised learning, Computer vision, Diabetic retinopathy, Weighted kappa
BibRef
Zhou, L.[Lei],
Zhao, Y.[Yu],
Yang, J.[Jie],
Yu, Q.[Qi],
Xu, X.[Xun],
Deep multiple instance learning for automatic detection of diabetic
retinopathy in retinal images,
IET-IPR(12), No. 4, April 2018, pp. 563-571.
DOI Link
1804
BibRef
Ghosh, S.K.[Swarup Kr],
Ghosh, A.[Anupam],
Chakrabarti, A.[Amlan],
VEA: Vessel Extraction Algorithm by Active Contour Model and a Novel
Wavelet Analyzer for Diabetic Retinopathy Detection,
IJIG(18), No. 02, 2018, pp. 1850008.
DOI Link
1804
BibRef
Kar, S.S.[Sudeshna Sil],
Maity, S.P.[Santi P.],
Gradation of diabetic retinopathy on reconstructed image using
compressed sensing,
IET-IPR(12), No. 11, November 2018, pp. 1956-1963.
DOI Link
1810
BibRef
Xiao, D.[Di],
Bhuiyan, A.[Alauddin],
Frost, S.[Shaun],
Vignarajan, J.[Janardhan],
Tay-Kearney, M.L.[Mei-Ling],
Kanagasingam, Y.[Yogesan],
Major automatic diabetic retinopathy screening systems and related core
algorithms: a review,
MVA(30), No. 3, April 2019, pp. 423-446.
Springer DOI
1906
BibRef
Leeza, M.[Mona],
Farooq, H.[Humera],
Detection of severity level of diabetic retinopathy using Bag of
features model,
IET-CV(13), No. 5, August 2019, pp. 523-530.
DOI Link
1908
BibRef
Verma, K.[Kesari],
Singh, B.K.[Bikesh Kumar],
Agrawal, N.[Neelam],
Non-invasive technique of diabetes detection using iris images,
IJCVR(9), No. 4, 2019, pp. 351-367.
DOI Link
1908
BibRef
Shankar, G.S.[G. Siva],
Manikandan, K.,
Diagnosis of diabetes diseases using optimized fuzzy rule set by grey
wolf optimization,
PRL(125), 2019, pp. 432-438.
Elsevier DOI
1909
Grey wolf optimization, Ant colony optimization, Fuzzy rules
BibRef
Khansari, M.M.,
Zhang, J.,
Qiao, Y.,
Gahm, J.K.,
Sarabi, M.S.,
Kashani, A.H.,
Shi, Y.,
Automated Deformation-Based Analysis of 3D Optical Coherence
Tomography in Diabetic Retinopathy,
MedImg(39), No. 1, January 2020, pp. 236-245.
IEEE DOI
2001
Optical coherence tomography, 3D image registration,
diabetic retinopathy, tensor-based morphometry
BibRef
Wang, J.L.[Jia-Liang],
Luo, J.[Jianxu],
Liu, B.[Bin],
Feng, R.[Rui],
Lu, L.[Lina],
Zou, H.[Haidong],
Automated diabetic retinopathy grading and lesion detection based on
the modified R-FCN object-detection algorithm,
IET-CV(14), No. 1, February 2020, pp. 1-8.
DOI Link
2002
BibRef
Li, X.M.[Xiao-Meng],
Hu, X.O.[Xia-Owei],
Yu, L.Q.[Le-Quan],
Zhu, L.[Lei],
Fu, C.W.[Chi-Wing],
Heng, P.A.[Pheng-Ann],
CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy
and Diabetic Macular Edema Grading,
MedImg(39), No. 5, May 2020, pp. 1483-1493.
IEEE DOI
2005
Diabetes, Task analysis, Feature extraction, Retinopathy,
Hemorrhaging, Biomedical imaging, Diabetic retinopathy,
attention mechanism
BibRef
Mousavi, E.[Elahe],
Kafieh, R.[Rahele],
Rabbani, H.[Hossein],
Classification of dry age-related macular degeneration and diabetic
macular oedema from optical coherence tomography images using
dictionary learning,
IET-IPR(14), No. 8, 19 June 2020, pp. 1571-1579.
DOI Link
2005
BibRef
Shankar, K.,
Sait, A.R.W.[Abdul Rahaman Wahab],
Gupta, D.[Deepak],
Lakshmanaprabu, S.K.,
Khanna, A.[Ashish],
Pandey, H.M.[Hari Mohan],
Automated detection and classification of fundus diabetic retinopathy
images using synergic deep learning model,
PRL(133), 2020, pp. 210-216.
Elsevier DOI
2005
Deep learning, Classification, Diabetic retinopathy,
Messidor dataset, Synergic deep learning
BibRef
Samanta, A.[Abhishek],
Saha, A.[Aheli],
Satapathy, S.C.[Suresh Chandra],
Fernandes, S.L.[Steven Lawrence],
Zhang, Y.D.[Yo-Dong],
Automated detection of diabetic retinopathy using convolutional
neural networks on a small dataset,
PRL(135), 2020, pp. 293-298.
Elsevier DOI
2006
Diabetic Retinopathy, CNN architecture, Colour fundus photography
BibRef
Gonzalez-Briceno, G.,
Sanchez, A.,
Ortega-Cisneros, S.,
Garcia Contreras, M.S.,
Pinedo Diaz, G.A.,
Moya-Sanchez, E.U.,
Artificial Intelligence-Based Referral System for Patients With
Diabetic Retinopathy,
Computer(53), No. 10, October 2020, pp. 77-87.
IEEE DOI
2009
BibRef
Nair, A.T.[Arun T.],
Muthuvel, K.,
Research Contributions with Algorithmic Comparison on the Diagnosis of
Diabetic Retinopathy,
IJIG(20), No. 4, October 2020, pp. 2050030.
DOI Link
2011
BibRef
Roshini, T.V.,
Ravi, R.V.[Ranjith V.],
Mathew, A.R.[A Reema],
Kadan, A.B.[Anoop Balakrishnan],
Subbian, P.S.[Perumal Sankar],
Automatic diagnosis of diabetic retinopathy with the aid of adaptive
average filtering with optimized deep convolutional neural network,
IJIST(30), No. 4, 2020, pp. 1173-1193.
DOI Link
2011
average adaptive filter, deep convolutional neural network,
diabetic retinopathy, diagnosis model,
fitness probability-based chicken swarm optimization
BibRef
He, A.,
Li, T.,
Li, N.,
Wang, K.,
Fu, H.,
CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy
Grading,
MedImg(40), No. 1, January 2021, pp. 143-153.
IEEE DOI
2012
Lesions, Task analysis, Feature extraction, Diabetes,
Machine learning, Image segmentation, Training,
global attention block (GAB)
BibRef
Zhou, Y.,
Wang, B.,
Huang, L.,
Cui, S.,
Shao, L.,
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading,
and Transferability,
MedImg(40), No. 3, March 2021, pp. 818-828.
IEEE DOI
2103
Image segmentation, Retinopathy, Transfer learning,
Benchmark testing, Diabetes, Lesions, Task analysis,
transfer learning
BibRef
Padmasini, N.[Natarajan],
Umamaheswari, R.[Rengasamy],
Automated detection of multiple structural changes of diabetic macular
oedema in SDOCT retinal images through transfer learning in CNNs,
IET-IPR(14), No. 16, 19 December 2020, pp. 4067-4075.
DOI Link
2103
BibRef
Lin, J.K.[Jin-Ke],
Cai, Q.L.[Qing-Ling],
Lin, M.Y.[Man-Ying],
Multi-Label Classification of Fundus Images With Graph Convolutional
Network and Self-Supervised Learning,
SPLetters(28), 2021, pp. 454-458.
IEEE DOI
2103
Task analysis, Training, Retinopathy, Feature extraction, Diabetes,
Convolution, Deep learning, Fundus images,
self-supervised learning
BibRef
Bhardwaj, C.[Charu],
Jain, S.[Shruti],
Sood, M.[Meenakshi],
Diabetic retinopathy severity grading employing quadrant-based
Inception-V3 convolution neural network architecture,
IJIST(31), No. 2, 2021, pp. 592-608.
DOI Link
2105
convolution neural network, data augmentation,
deep neural network, diabetic retinopathy, hand-crafted features
BibRef
Sambyal, N.[Nitigya],
Saini, P.[Poonam],
Syal, R.[Rupali],
Gupta, V.[Varun],
Aggregated residual transformation network for multistage
classification in diabetic retinopathy,
IJIST(31), No. 2, 2021, pp. 741-752.
DOI Link
2105
aggregated residual transformations, computer-aided diagnosis,
convolutional neural networks, diabetic retinopathy,
ResNeXt
BibRef
Kadan, A.B.[Anoop Balakrishnan],
Subbian, P.S.[Perumal Sankar],
Optimized hybrid classifier for diagnosing diabetic retinopathy:
Iterative blood vessel segmentation process,
IJIST(31), No. 2, 2021, pp. 1009-1033.
DOI Link
2105
convolution neural network, diabetic retinopathy,
improvement counter-based rider optimization algorithm,
retinal fundus images
BibRef
Shivsharan, N.[Nitin],
Ganorkar, S.[Sanjay],
Diabetic Retinopathy Detection Using Optimization Assisted Deep
Learning Model: Outlook on Improved Grey Wolf Algorithm,
IJIG(21), No. 3, July 2021, pp. 2150035.
DOI Link
2107
BibRef
Deepa, V.,
Kumar, C.S.[C. Sathish],
Andrews, S.S.[Sheena Susan],
Fusing dual-tree quaternion wavelet transform and local mesh based
features for grading of diabetic retinopathy using extreme learning
machine classifier,
IJIST(31), No. 3, 2021, pp. 1625-1637.
DOI Link
2108
diabetic retinopathy, extreme learning machine classifier,
local mesh patterns, quaternion wavelet transform, textural features
BibRef
Boukadida, R.[Rahma],
Elloumi, Y.[Yaroub],
Akil, M.[Mohamed],
Bedoui, M.H.[Mohamed Hedi],
Mobile-aided screening system for proliferative diabetic retinopathy,
IJIST(31), No. 3, 2021, pp. 1638-1654.
DOI Link
2108
fundus images, mobile health,
mobile-aided-screening (MAS) system, neovascularization,
smartphone captured fundus image
BibRef
Luo, X.L.[Xiao-Ling],
Pu, Z.[Zuhui],
Xu, Y.[Yong],
Wong, W.K.[Wai Keung],
Su, J.[Jingyong],
Dou, X.Y.[Xiao-Yan],
Ye, B.[Baikang],
Hu, J.[Jiying],
Mou, L.[Lisha],
MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs
and attention mechanisms,
PR(120), 2021, pp. 108104.
Elsevier DOI
2109
Diabetic retinopathy (DR),
Deep convolutional neural networks (DCNNs), Multi-view, Classification
BibRef
Imran, A.A.Z.[Abdullah-Al-Zubaer],
Terzopoulos, D.[Demetri],
Progressive Adversarial Semantic Segmentation,
ICPR21(4910-4917)
IEEE DOI
2105
Training, Image segmentation, Retinopathy, Semantics, Lung,
Data models, Diabetes, segmentation, adversarial learning,
pulmonary X-ray
BibRef
Niri, R.[Rania],
Douzi, H.[Hassan],
Lucas, Y.[Yves],
Treuillet, S.[Sylvie],
A Superpixel-wise Fully Convolutional Neural Network Approach for
Diabetic Foot Ulcer Tissue Classification,
AIHA20(308-320).
Springer DOI
2103
BibRef
Liu, Q.,
Zhao, J.,
The Classification of Diabetic Foot Based on Faster,
CVIDL20(585-591)
IEEE DOI
2102
convolutional neural nets, diseases, feature extraction,
Gaussian noise, image classification, medical image processing,
Convolutional neural network
BibRef
Luo, D.,
Shen, L.,
Vessel-Net: A Vessel-Aware Ensemble Network For Retinopathy Screening
From Fundus Image,
ICIP20(320-324)
IEEE DOI
2011
Streaming media, Retinopathy, Biomedical imaging, Training,
Feature extraction, Image segmentation, Deep learning,
retinal vessel segmentation
BibRef
Zhao, Z.Y.[Zi-Yuan],
Chopra, K.[Kartik],
Zeng, Z.[Zeng],
Li, X.L.[Xiao-Li],
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
BibRef
Zhao, Z.Y.[Zi-Yuan],
Zhang, K.[Kerui],
Hao, X.J.[Xue-Jie],
Tian, J.[Jing],
Chua, M.C.H.[Matthew Chin Heng],
Chen, L.[Li],
Xu, X.[Xin],
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading,
ICIP19(1385-1389)
IEEE DOI
1910
BibRef
Rania, N.[Niri],
Douzi, H.[Hassan],
Yves, L.[Lucas],
Sylvie, T.[Treuillet],
Semantic Segmentation of Diabetic Foot Ulcer Images:
Dealing with Small Dataset in Dl Approaches,
ICISP20(162-169).
Springer DOI
2009
BibRef
Carvalho, C.[Catarina],
Pedrosa, J.[João],
Maia, C.[Carolina],
Penas, S.[Susana],
Carneiro, Â.[Ângela],
Mendonça, L.[Luís],
Mendonça, A.M.[Ana Maria],
Campilho, A.[Aurélio],
A Multi-dataset Approach for DME Risk Detection in Eye Fundus Images,
ICIAR20(II:285-298).
Springer DOI
2007
Diabetic macular edema
BibRef
Costa, P.,
Araújo, T.,
Aresta, G.,
Galdran, A.,
Mendonça, A.M.,
Smailagic, A.,
Campilho, A.,
EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks
for Diabetic Retinopathy Detection,
MVA19(1-6)
DOI Link
1806
biomedical optical imaging, convolutional neural nets, diseases,
eye, feature extraction, image classification,
Predictive models
BibRef
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,
Blood Vessels Segmentation, Retinal Vasculature Features
BibRef
Kind, A.[Adrian],
Azzopardi, G.[George],
An Explainable AI-Based Computer Aided Detection System for Diabetic
Retinopathy Using Retinal Fundus Images,
CAIP19(I:457-468).
Springer DOI
1909
BibRef
Smailagic, A.[Asim],
Sharan, A.[Anupma],
Costa, P.[Pedro],
Galdran, A.[Adrian],
Gaudio, A.[Alex],
Campilho, A.[Aurélio],
Learned Pre-processing for Automatic Diabetic Retinopathy Detection on
Eye Fundus Images,
ICIAR19(II:362-368).
Springer DOI
1909
BibRef
Xiao, Q.[Qiqi],
Zou, J.[Jiaxu],
Yang, M.[Muqiao],
Gaudio, A.[Alex],
Kitani, K.[Kris],
Smailagic, A.[Asim],
Costa, P.[Pedro],
Xu, M.[Min],
Improving Lesion Segmentation for Diabetic Retinopathy Using
Adversarial Learning,
ICIAR19(II:333-344).
Springer DOI
1909
BibRef
Hernández, A.[Abián],
Arteaga-Marrero, N.[Natalia],
Villa, E.[Enrique],
Fabelo, H.[Himar],
Callicó, G.M.[Gustavo M.],
Ruiz-Alzola, J.[Juan],
Automatic Segmentation Based on Deep Learning Techniques for Diabetic
Foot Monitoring Through Multimodal Images,
CIAP19(II:414-424).
Springer DOI
1909
BibRef
Eladawi, N.,
Elmogy, M.,
Fraiwan, L.,
Pichi, F.,
Ghazal, M.,
Aboelfetouh, A.,
Riad, A.,
Keynton, R.,
Schaal, S.,
El-Baz, A.,
Early Diagnosis of Diabetic Retinopathy in OCTA Images Based on Local
Analysis of Retinal Blood Vessels and Foveal Avascular Zone,
ICPR18(3886-3891)
IEEE DOI
1812
Retina, Feature extraction, Biomedical imaging, Blood vessels,
Image segmentation, Support vector machines
BibRef
Colomer, A.[Adrián],
Ruiz, P.[Pablo],
Naranjo, V.[Valery],
Molina, R.[Rafael],
Katsaggelos, A.K.[Aggelos K.],
Hard Exudate Detection Using Local Texture Analysis and Gaussian
Processes,
ICIAR18(639-649).
Springer DOI
1807
BibRef
Neira-Tovar, L.[Leticia],
Rodriguez, I.C.[Ivan Castilla],
The Use of Virtual and Augmented Reality to Prevent the Physical
Effects Caused by Diabetes Melitus Type 2: An Integrative Review,
VAMR18(II: 126-133).
Springer DOI
1807
BibRef
Bourouis, S.[Sami],
Zaguia, A.[Atef],
Bouguila, N.[Nizar],
Hybrid Statistical Framework for Diabetic Retinopathy Detection,
ICIAR18(687-694).
Springer DOI
1807
BibRef
Melo, T.[Tânia],
Mendonça, A.M.[Ana Maria],
Campilho, A.[Aurélio],
Creation of Retinal Mosaics for Diabetic Retinopathy Screening: A
Comparative Study,
ICIAR18(669-678).
Springer DOI
1807
BibRef
Gondal, W.M.,
Köhler, J.M.,
Grzeszick, R.,
Fink, G.A.,
Hirsch, M.,
Weakly-supervised localization of diabetic retinopathy lesions in
retinal fundus images,
ICIP17(2069-2073)
IEEE DOI
1803
Biomedical imaging, Cams, Diabetes, Lesions, Retina, Retinopathy,
Training, deep learning, diabetic retinopathy, lesion detection,
weakly-supervised object localization
BibRef
Komi, M.,
Li, J.[Jun],
Zhai, Y.X.[Yong-Xin],
Zhang, X.G.[Xian-Guo],
Application of data mining methods in diabetes prediction,
ICIVC17(1006-1010)
IEEE DOI
1708
Biological system modeling, Biomedical imaging, Blood,
Computational modeling, Diabetes, Diseases,
Support vector machines, classification, data mining, diabetes, prediction
BibRef
Costa, P.,
Campilho, A.,
Convolutional bag of words for diabetic retinopathy detection from
eye fundus images,
MVA17(165-168)
DOI Link
1708
Encoding, Feature extraction, Lesions, Pathology, Retina, Retinopathy, Visualization
BibRef
Massich, J.,
Rastgoo, M.,
Lemaître, G.,
Cheung, C.Y.,
Wong, T.Y.,
Sidibé, D.,
Mériaudeau, F.,
Classifying DME vs normal SD-OCT volumes: A review,
ICPR16(1297-1302)
IEEE DOI
1705
Diabetes, Feature extraction, Histograms, Pathology,
Principal component analysis, Retina, Testing,
Diabetic Macular Edema (DME), Machine Learning (ML),
Spectral Domain OCT (SD-OCT), benchmark
BibRef
Soomro, T.A.[Toufique A.],
Gao, J.B.[Jun-Bin],
Khan, M.A.U.[Mohammad A.U.],
Khan, T.M.[Tariq M.],
Paul, M.[Manoranjan],
Role of Image Contrast Enhancement Technique for Ophthalmologist as
Diagnostic Tool for Diabetic Retinopathy,
DICTA16(1-8)
IEEE DOI
1701
Biomedical imaging
BibRef
Kaur, A.,
Kaur, P.,
An integrated approach for Diabetic Retinopathy exudate segmentation
by using Genetic Algorithm and Switching Median Filter,
ICIVC16(119-123)
IEEE DOI
1610
ant colony optimisation
BibRef
Agurto, C.,
Chek, V.,
Edwards, A.,
Jarry, Z.,
Barriga, S.,
Simon, J.,
Soliz, P.,
A thermoregulation model to detect diabetic peripheral neuropathy,
Southwest16(13-16)
IEEE DOI
1605
Biological system modeling
BibRef
Laaksonen, L.[Lauri],
Hannuksela, A.,
Claridge, E.[Ela],
Fält, P.[Pauli],
Hauta-Kasari, M.[Markku],
Uusitalo, H.[Hannu],
Lensu, L.[Lasse],
Evaluation of feature sensitivity to training data inaccuracy in
detection of retinal lesions,
IPTA16(1-6)
IEEE DOI
1703
edge detection
BibRef
Nguyen, U.,
Laaksonen, L.[Lauri],
Uusitalo, H.[Hannu],
Lensu, L.[Lasse],
Reconstruction of retinal spectra from RGB data using a RBF network,
IPTA16(1-6)
IEEE DOI
1703
image colour analysis
BibRef
Fält, P.[Pauli],
Yamaguchi, M.[Masahiro],
Murakami, Y.[Yuri],
Laaksonen, L.[Lauri],
Lensu, L.[Lasse],
Claridge, E.[Ela],
Hauta-Kasari, M.[Markku],
Uusitalo, H.[Hannu],
Multichannel Spectral Image Enhancement for Visualizing Diabetic
Retinopathy Lesions,
ICISP14(52-60).
Springer DOI
1406
BibRef
Velu, C.M.,
Kashwan, K.R.,
Multi-Level Counter Propagation Network for diabetes classification,
ICSIPR13(190-194).
IEEE DOI
1304
BibRef
Akram, M.U.[M. Usman],
Tariq, A.[Anam],
Khan, S.A.[Shoab A.],
Detection of Neovascularization for Screening of Proliferative Diabetic
Retinopathy,
ICIAR12(II: 372-379).
Springer DOI
1206
BibRef
Carranza, C.[Cesar],
Murray, V.[Victor],
Pattichis, M.[Marios],
Barriga, E.S.[E. Simon],
Multiscale AM-FM decompositions with GPU acceleration for diabetic
retinopathy screening,
Southwest12(121-124).
IEEE DOI
1205
BibRef
Yu, H.G.[Hong-Gang],
Agurto, C.[Carla],
Barriga, S.[Simon],
Nemeth, S.C.[Sheila C.],
Soliz, P.[Peter],
Zamora, G.[Gilberto],
Automated image quality evaluation of retinal fundus photographs in
diabetic retinopathy screening,
Southwest12(125-128).
IEEE DOI
1205
BibRef
Poddar, S.[Sunrita],
Jha, B.K.[Bibhash Kumar],
Chakraborty, C.[Chandan],
Quantitative clinical marker extraction from colour fundus images for
non-proliferative Diabetic Retinopathy grading,
ICIIP11(1-6).
IEEE DOI
1112
BibRef
Yano, V.[Vitor],
Ferrari, G.[Giselle],
Zimmer, A.[Alessandro],
Using the Pupillary Reflex as a Diabetes Occurrence Screening Aid Tool
through Neural Networks,
ICIAR11(II: 40-47).
Springer DOI
1106
BibRef
Ferreira, A.[Ana],
Morgado, A.M.[António Miguel],
Silva, J.S.[José Silvestre],
Automatic Corneal Nerves Recognition for Earlier Diagnosis and
Follow-Up of Diabetic Neuropathy,
ICIAR10(II: 60-69).
Springer DOI
1006
BibRef
Hann, C.E.,
Narbot, M.,
MacAskill, M.,
Diabetic Retinopathy detection using geometrical techniques related to
the underlying physiology,
IVCNZ10(1-8).
IEEE DOI
1203
BibRef
Ravishankar, S.[Saiprasad],
Jain, A.[Arpit],
Mittal, A.[Anurag],
Automated feature extraction for early detection of diabetic
retinopathy in fundus images,
CVPR09(210-217).
IEEE DOI
0906
BibRef
Fält, P.[Pauli],
Hiltunen, J.[Jouni],
Hauta-Kasari, M.[Markku],
Sorri, I.[Iiris],
Kalesnykiene, V.[Valentina],
Uusitalo, H.[Hannu],
Extending Diabetic Retinopathy Imaging from Color to Spectra,
SCIA09(149-158).
Springer DOI
0906
BibRef
Kauppi, T.,
Kalesnykiene, V.,
Kamarainen, J.K.,
Lensu, L.,
Sorri, I.,
Raninen, A.,
Voutilainen, R.,
Uusitalo, H.,
Kalviainen, H.,
Pietila, J.,
The DIARETDB1 diabetic retinopathy database and evaluation protocol,
BMVC07(xx-yy).
PDF File.
0709
Dataset, Retina.
BibRef
Estabridis, K.[Katia],
de Figueiredo, R.J.P.[Rui J. P.],
Automatic Detection and Diagnosis of Diabetic Retinopathy,
ICIP07(II: 445-448).
IEEE DOI
0709
BibRef
Xu, X.Y.[Xin-Yu],
Li, B.X.[Bao-Xin],
Florez, J.F.[Jose F.],
Li, H.K.[Helen K.],
Simulation of Diabetic Retinopathy Neovascularization in Color Digital
Fundus Images,
ISVC06(I: 421-433).
Springer DOI
0611
BibRef
Zhang, X.H.[Xiao-Hui],
Chutatape, O.[Opas],
Top-Down and Bottom-Up Strategies in Lesion Detection of Background
Diabetic Retinopathy,
CVPR05(II: 422-428).
IEEE DOI
0507
BibRef
Earlier:
Detection and classification of bright lesions in color fundus images,
ICIP04(I: 139-142).
IEEE DOI
0505
BibRef
Osareh, A.[Alireza],
Shadgar, B.[Bita],
Markham, R.[Richard],
Comparative Pixel-Level Exudate Recognition in Colour Retinal Images,
ICIAR05(894-902).
Springer DOI
0509
BibRef
Osareh, A.,
Mirmehdi, M.,
Thomas, B.,
Markham, R.,
Comparison of colour spaces for optic disc localisation in retinal
images,
ICPR02(I: 743-746).
IEEE DOI
0211
BibRef
Earlier:
Classification and Localisation of Diabetic-Related Eye Disease,
ECCV02(IV: 502 ff.).
Springer DOI
0205
BibRef
Butikova, J.,
Bocchi, L.,
Freivalds, T.,
Texture analysis and optical anisotropy measurements of leukocytes for
early diagnostics of diabetes mellitus,
ICIP03(I: 1081-1084).
IEEE DOI
0312
BibRef
Byrne, M.J.,
Graham, J.,
Application of Model Based Image Interpretation Methods
of Diabetic Neuropathy,
ECCV96(II:272-282).
Springer DOI
BibRef
9600
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Macular Degeneration Detection, Retinal Analysis Application .