21.5.6 Diabetic Retinopathy, Retinal Analysis Application

Chapter Contents (Back)
Retinal Images. Diabetic Retinopathy. Eye.
See also Retinal Images, Angiography, Blood Vessels in the Eye.
See also Retinal Microaneurysms, Detection.

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
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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.,
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JOSA-A(24), No. 5, May 2007, pp. 1448-1456.
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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
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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
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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.
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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
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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.
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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
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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, 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
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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
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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
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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
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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
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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
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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
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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
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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.
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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

Hacisoftaoglu, R.E.[Recep E.], Karakaya, M.[Mahmut], Sallam, A.B.[Ahmed B.],
Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems,
PRL(135), 2020, pp. 409-417.
Elsevier DOI 2006
Deep learning, Diabetic retinopathy, Smartphone-based retinal imaging, AlexNet, GoogLeNet, ResNet50 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
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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
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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.H.[Zu-Hui], Xu, Y.[Yong], Wong, W.K.[Wai Keung], Su, J.Y.[Jing-Yong], 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

Bourouis, S.[Sami], Bouguila, N.[Nizar],
Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis,
IJIST(31), No. 4, 2021, pp. 1989-2002.
DOI Link 2112
diabetic retinopathy, images classification, infinite mixture models, nonparametric Bayesian learning, shifted-scaled Dirichlet distribution BibRef

Mathews, M.R.[Mili Rosline], Anzar, S.M.,
A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema,
IJIST(31), No. 4, 2021, pp. 2093-2122.
DOI Link 2112
computer aided diagnosis, convolutional neural networks, deep learning, diabetic macular edema, diabetic retinopathy, retinal fundus imaging BibRef

Pappu, G.P.[Geetha Pavani], Biswal, B.[Birendra], Gandhi, T.K.[Tapan K.], Ram, M.V.S.S.[Metta Venkata Satya Sai],
Classification of neovascularization on retinal images using extreme learning machine,
IJIST(31), No. 3, 2021, pp. 1536-1550.
DOI Link 2108
cross-validation, datasets, diabetic retinopathy, extreme learning machine classifier, Frangi filter, sequential recursive feature elimination BibRef

Athalye, S.S.[Saurabh Shrikant], Vijay, G.[Gaurav],
Taylor series-based deep belief network for automatic classification of diabetic retinopathy using retinal fundus images,
IJIST(32), No. 3, 2022, pp. 882-901.
DOI Link 2205
adaptive thresholding, binarization, deep belief network (DBN), diabetic retinopathy (DR), wavelet transform BibRef

Ahmed, Z.[Zeeshan], Panhwar, S.Q.[Shahbaz Qamar], Baqai, A.[Attiya], Umrani, F.A.[Fahim Aziz], Ahmed, M.[Munawar], Khan, A.[Arbaaz],
Deep learning based automated detection of intraretinal cystoid fluid,
IJIST(32), No. 3, 2022, pp. 902-917.
DOI Link 2205
artificial intelligence (AI), cystoid macular edema (CME), deep learning (DL), diabetic macular edema (DME), optical coherence tomography (OCT) BibRef

Huang, S.Q.[Shi-Qi], Li, J.A.[Jian-An], Xiao, Y.Z.[Yu-Ze], Shen, N.[Ning], Xu, T.F.[Ting-Fa],
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation,
MedImg(41), No. 6, June 2022, pp. 1596-1607.
IEEE DOI 2206
Lesions, Transformers, Image segmentation, Pathology, Task analysis, Head, Feature extraction, Diabetic retinopathy, fundus image, deep learning BibRef

Nagaraj, P.[Palanigurupackiam], Deepalakshmi, P.[Perumalsamy],
An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis,
IJIST(32), No. 4, 2022, pp. 1373-1396.
DOI Link 2207
decision tree, diabetes, expert recommendation system, fuzzy inference system, fuzzy logic, IFIR_PDDM BibRef

Wang, C.L.[Cun-Lei], Li, D.H.[Dong-Hui],
Diabetes Noninvasive Recognition via Improved Capsule Network,
IEICE(E105-D), No. 8, August 2022, pp. 1464-1471.
WWW Link. 2207
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Ahourag, A.[Abdellah], El Moutaouakil, K.[Karim], Chellak, S.[Saliha], Baizri, H.[Hicham], Cheggour, M.[Mouna],
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 BibRef

Yadav, N.[Nirmal],
A deep data-driven approach for enhanced segmentation of blood vessel for diabetic retinopathy,
IJIST(32), No. 5, 2022, pp. 1696-1708.
DOI Link 2209
deep learning model, neural networks, radon transform, wavelet transform BibRef

Yu, M.[Moye], Wang, Y.[Yi],
Intelligent detection and applied research on diabetic retinopathy based on the residual attention network,
IJIST(32), No. 5, 2022, pp. 1789-1800.
DOI Link 2209
artificial intelligence, attention mechanism, CNN, diabetic retinopathy, dilated convolution, fundus image BibRef

Tajudin, N.M.A.[Nurul Mirza Afiqah], Kipli, K.[Kuryati], 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], Hoque, M.E.[Mohammed Enamul],
Deep learning in the grading of diabetic retinopathy: A review,
IET-CV(16), No. 8, 2022, pp. 667-682.
DOI Link 2210
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Yang, Y.[Yehui], Shang, F.X.[Fang-Xin], Wu, B.[Binghong], Yang, D.[Dalu], Wang, L.[Lei], Xu, Y.[Yanwu], Zhang, W.S.[Wen-Sheng], Zhang, T.Z.[Tian-Zhu],
Robust Collaborative Learning of Patch-Level and Image-Level Annotations for Diabetic Retinopathy Grading From Fundus Image,
Cyber(52), No. 11, November 2022, pp. 11407-11417.
IEEE DOI 2211
Lesions, Annotations, Generators, Feature extraction, Image segmentation, Retinopathy, Diabetes, Collaborative learning, fundus image BibRef

Padmapriya, M., Pasupathy, S.,
Supervised learning software model for the diagnosis of diabetic retinopathy,
IJCVR(13), No. 1, 2023, pp. 116-132.
DOI Link 2212
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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 BibRef

Naveen, J., Selvam, S.[Sheba], Selvam, B.[Blessy],
FO-DPSO Algorithm for Segmentation and Detection of Diabetic Mellitus for Ulcers,
IJIG(23), No. 3 2023, pp. 2240011.
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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 BibRef

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 BibRef

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 transformer for segmentation of hard exudates in diabetes retinopathy,
IET-IPR(17), No. 11, 2023, pp. 3337-3348.
DOI Link 2310
convolutional neural nets, image segmentation, medical image processing BibRef

Xia, X.[Xue], Zhan, K.[Kun], Fang, Y.M.[Yu-Ming], Jiang, W.H.[Wen-Hui], Shen, F.[Fei],
Lesion-aware network for diabetic retinopathy diagnosis,
IJIST(33), No. 6, 2023, pp. 1914-1928.
DOI Link 2311
attention mechanism, diabetic retinopathy screening, fundus image analysis, lesion segmentation, multi-task learning BibRef

Almattar, W.[Wadha], Luqman, H.[Hamzah], Khan, F.A.[Fakhri Alam],
Diabetic retinopathy grading review: Current techniques and future directions,
IVC(139), 2023, pp. 104821.
Elsevier DOI 2311
Diabetic retinopathy, Retinal fundus images, Diabetic retinopathy stages, Computer-aided diagnosis, Machine learning BibRef

Radha, K., Karuna, Y.[Yepuganti],
Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey,
IJIST(34), No. 1, 2024, pp. e22945.
DOI Link 2401
automatic segmentation techniques, blood vessel detection, diabetes, diabetic retinopathy, DR detection, handcrafted segmentation BibRef

Bapatla, S.[Sesikala], Harikiran, J.,
Deer Hunting Optimization with 3D-Convolutional Neural Network for Diabetic Retinopathy Classification Model,
IJIG(24), No. 1, Januaur 2024, pp. 2450009.
DOI Link 2402
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Macsik, P.[Peter], Pavlovicova, J.[Jarmila], Kajan, S.[Slavomir], Goga, J.[Jozef], Kurilova, V.[Veronika],
Image preprocessing-based ensemble deep learning classification of diabetic retinopathy,
IET-IPR(18), No. 3, 2024, pp. 807-828.
DOI Link 2402
biomedical optical imaging, computer vision, convolutional neural nets, image classification, medical image processing BibRef


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A Complete AI-based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management,
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Guo, W.J.[Wen-Jie], Guo, R.S.[Rui-Shu], Du, X.N.[Xin-Nong], Zhao, Y.C.[Yu-Chen], Li, X.Y.[Xiu-Yuan], 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], Shao, H.C.A.[Hao-Chi-Ang], Lin, C.W.[Chia-Wen],
Vessel Segmentation and Dirt/Reflection Detection For Retinal Fundus Photographs,
ICIP22(3953-3957)
IEEE DOI 2211
Training, Image segmentation, Codes, Retinopathy, Retina, Generators, Diabetes, diabetic retinopathy (DR), retinal fundus image, reconstruction BibRef

Nandy, J.[Jay], Hs, W.[Wynne], Le, M.L.[Mong Li],
Distributional Shifts In Automated Diabetic Retinopathy Screening,
ICIP21(255-259)
IEEE DOI 2201
Training, Retinopathy, Image processing, Detectors, Retina, Diabetes, Distributional Shift, Dirichlet Prior Network, Out-of-distribution BibRef

Ioannou, G.[George], Papagiannis, T.[Tasos], Tagaris, T.[Thanos], Alexandridis, G.[Georgios], Stafylopatis, A.[Andreas],
Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images,
MIA-COVID19D21(480-486)
IEEE DOI 2112
Training, Image segmentation, Visualization, Retinopathy, Computational modeling, Transforms BibRef

Sun, R.[Rui], Li, Y.H.[Yi-Hao], Zhang, T.Z.[Tian-Zhu], Mao, Z.D.[Zhen-Dong], Wu, F.[Feng], Zhang, Y.D.[Yong-Dong],
Lesion-Aware Transformers for Diabetic Retinopathy Grading,
CVPR21(10933-10942)
IEEE DOI 2111
Retinopathy, Sociology, Benchmark testing, Transformers, Decoding, Diabetes, Pattern recognition 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
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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.Q.[Qi-Qi], Zou, J.X.[Jia-Xu], Yang, M.Q.[Mu-Qiao], 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
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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
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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],
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ICISP14(52-60).
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Velu, C.M., Kashwan, K.R.,
Multi-Level Counter Propagation Network for diabetes classification,
ICSIPR13(190-194).
IEEE DOI 1304
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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
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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

Murray, V.[Victor], Agurto, C.[Carla], Barriga, S.[Simon], Pattichis, M.S.[Marios S.], Soliz, P.[Peter],
Real-time diabetic retinopathy patient screening using multiscale AM-FM methods,
ICIP12(525-528).
IEEE DOI 1302
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
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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
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Hann, C.E., Narbot, M., MacAskill, M.,
Diabetic Retinopathy detection using geometrical techniques related to the underlying physiology,
IVCNZ10(1-8).
IEEE DOI 1203
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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, AMD, Retinal Analysis Application .


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