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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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],
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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.
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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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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.X.[Jian-Xu], 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.
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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, January 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, convolutional neural nets, image classification, medical image processing BibRef

Ji, Z.X.[Ze-Xuan], Ma, X.[Xiao], Leng, T.[Theodore], Rubin, D.L.[Daniel L.], Chen, Q.[Qiang],
Mirrored X-Net: Joint classification and contrastive learning for weakly supervised GA segmentation in SD-OCT,
PR(153), 2024, pp. 110507.
Elsevier DOI Code:
WWW Link. 2405
Age-related macular degeneration, Geographic atrophy segmentation, SD-OCT images, Multitask learning BibRef

Joseph, J.[Jovi], Sreela, S.R.,
MODCN: Fine-Tuned Deep Convolutional Neural Network with GAN Deployed to Forecast Diabetic Eye Damage in Fundus Retinal Images,
IJIG(24), No. 3, May 2024, pp. 2450029.
DOI Link 2406
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Kulkarni, G.[Girish], Manike, C.[Chiranjeevi],
Heuristic-Based Ensemble Model Selection Strategy with Parameter Tuning for Optimal Diabetes Mellitus Prediction,
IJIG(24), No. 4, July 2024, pp. 2450046.
DOI Link 2408
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Zhang, Y.H.[Yu-Han], Ma, X.[Xiao], Huang, K.[Kun], Li, M.C.[Ming-Chao], Heng, P.A.[Pheng-Ann],
Semantic-Oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images,
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, fundus images BibRef

Rawat, A.[Amit], Singh, M.P.[Maheshwari Prasad], Sharma, R.R.[Rishi Raj],
Data-driven 2D-EWT based diabetic retinopathy identification using hybrid neural network,
IVC(150), 2024, pp. 105194.
Elsevier DOI 2409
Diabetic retinopathy, Fundus disease, Empirical wavelet transform, Neural network, Magnitude spectrum BibRef

Devi, Y.S.[Yerrarapu Sravani], Kumar, S.P.[S. Phani],
Diabetic Retinopathy (DR) Image Synthesis Using DCGAN and Classification of DR Using Transfer Learning Approaches,
IJIG(24), No. 5, September 2024, pp. 2340009.
DOI Link 2410
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Gargi, M., Namburu, A.[Anupama],
Severity Detection of Diabetic Retinopathy: A Review,
IJIG(24), No. 5, September 2024, pp. 2340007.
DOI Link 2410
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Sachdeva, J.[Jainy], Mishra, P.[Puneet], Katoch, D.[Deeksha],
Diabetic retinopathy data augmentation and vessel segmentation through deep learning based three fully convolution neural networks,
IVC(151), 2024, pp. 105284.
Elsevier DOI 2411
Fully convolutional neural networks (FCNN), Difference-of-Gaussian (DoG), Segmentation, Retinal blood vessels BibRef

Soren, V.N.[Vivek Noel], Prajwal, H.S., Sundaresan, V.[Vaanathi],
Automated grading of diabetic retinopathy and Radiomics analysis on ultra-wide optical coherence tomography angiography scans,
IVC(151), 2024, pp. 105292.
Elsevier DOI 2411
Diabetic retinopathy, Proliferative, Deep learning, Radiomics BibRef

Zhang, J.C.[Jia-Cheng], Jin, R.[Rong], Liu, W.Q.[Wen-Qiang],
Enhancing semi-supervised contrastive learning through saliency map for diabetic retinopathy grading,
IET-CV(18), No. 8, 2024, pp. 1127-1137.
DOI Link Code:
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convolutional neural nets, learning (artificial intelligence), medical image processing BibRef

Ma, Y.[Yuan], Gu, Y.[Yang], Guo, S.[Shuai], Qin, X.[Xin], Wen, S.J.[Shi-Jie], Shi, N.F.[Nian-Feng], Dai, W.W.[Wei-Wei], Chen, Y.Q.[Yi-Qiang],
Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading,
MedImg(44), No. 3, March 2025, pp. 1115-1126.
IEEE DOI 2503
Feature extraction, Diabetic retinopathy, Lesions, Training, Risk minimization, Diseases, Adaptation models, Minimization, transfer learning BibRef

Butt, M.[Mohsin], NurFatimah, D.N.F., Khan, M.A.[Majid Ali], Latif, G.[Ghazanfar], Bashar, A.[Abul],
MEDCnet: A Memory Efficient Approach for Processing High-Resolution Fundus Images for Diabetic Retinopathy Classification Using CNN,
IJIST(35), No. 2, 2025, pp. e70063.
DOI Link 2504
convolutional neural networks (CNN), deep learning, diabetic retinopathy, divide and conquer CNN, fundus images, performance optimization BibRef

Bi, Q.[Qi], Yi, J.J.[Jing-Jun], Zheng, H.[Hao], Zhan, H.L.[Hao-Lan], Huang, Y.W.[Ya-Wen], Ji, W.[Wei], Li, Y.X.[Yue-Xiang], Zheng, Y.F.[Ye-Feng],
GAD: Domain generalized diabetic retinopathy grading by grade-aware de-stylization,
PR(164), 2025, pp. 111484.
Elsevier DOI 2504
Diabetic retinopathy grading, Domain generalization, De-stylization, Contrastive learning BibRef

Lin, Y.X.[Yu-Xin], Dou, X.Y.[Xiao-Yan], Luo, X.L.[Xiao-Ling], Wu, Z.H.[Zhi-Hao], Liu, C.L.[Cheng-Liang], Luo, T.Y.[Tian-Yi], Wen, J.[Jie], Ling, B.W.K.[Bingo Wing-Kuen], Xu, Y.[Yong], Wang, W.[Wei],
Multi-view diabetic retinopathy grading via cross-view spatial alignment and adaptive vessel reinforcing,
PR(164), 2025, pp. 111487.
Elsevier DOI 2504
Diabetic retinopathy detection, Multi-view model, Vision transformer, Information reinforce, Feature fusion BibRef

Bhat, P.[Padmanayana], Anoop, B.K.,
Improved Invasive Weed Social Ski-Driver Optimization-Based Deep Convolution Neural Network for Diabetic Retinopathy Classification,
IJIG(25), No. 2, March 2025, pp. 2550012.
DOI Link 2504
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Soomro, D.B.[Danyal Badar], ChengLiang, W.[Wang], Ashraf, M.[Mahmood], AlHammadi, D.A.[Dina Abdulaziz], Alsubai, S.[Shtwai], Medaglia, C.[Carlo], Innab, N.[Nisreen], Umer, M.[Muhammad],
Automated dual CNN-based feature extraction with SMOTE for imbalanced diabetic retinopathy classification,
IVC(159), 2025, pp. 105537.
Elsevier DOI 2505
Diabetic retinopathy classification, Deep learning, Hybrid feature extraction, Transfer learning, SMOTE, Automated CNN BibRef

Minghui, C.[Chen], Shi-Yi, X.[Xu], Jing, Z.[Zhou], Hongwang, W.[Wang], Yi, S.[Shao],
An Enhanced Double-Unet Segmentation Framework for the Early Detection of Diabetic Retinopathy,
IJIST(35), No. 3, 2025, pp. e70096.
DOI Link 2506
attention gate, deep learning, dense Atrous spatial pyramid pooling, diabetic retinopathy BibRef

Zhang, J.[Jie], Jiang, X.Y.[Xiang-Yu], Ni, S.T.[Shu-Ting], Liu, S.[Shuang], Zou, W.[Wei],
Microaneurysm Segmentation Method for Diabetic Retinopathy Fundus Lesions Based on the Multi-Scale Feature Subtraction Fusion Network,
IJIST(35), No. 4, 2025, pp. e70140.
DOI Link 2507
feature fusion, multi-scale feature compensation, wavelet transform BibRef

Chokuwa, S.[Sharon], Khan, M.H.[Muhammad Haris],
Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy Grading,
WACV25(3667-3677)
IEEE DOI 2505
Deep learning, Manifolds, Diabetic retinopathy, Risk minimization, Noise, Observers, Vectors, Robustness, Standards BibRef

Takezaki, S.[Shumpei], Tanaka, K.[Kiyohito], Uchida, S.[Seiichi],
Self-Relaxed Joint Training: Sample Selection for Severity Estimation with Ordinal Noisy Labels,
WACV25(368-377)
IEEE DOI Code:
WWW Link. 2505
Training, Diabetic retinopathy, Accuracy, Codes, Estimation, Retina, Noise measurement, Medical diagnostic imaging, ordinal classification BibRef

Soliz, P.[Peter], Wigdahl, J.[Jeff], Soliz, S.[Sarah], Saint-Lot, S.[Sheraz], Bhandarkar, A.[Adhitya], Bhatia, S.[Sooraj], Duran-Valdez, E.[Elizabeth], Schade, D.S.[David S.],
Computer-Based Diagnosis of Early Signs of Diabetic Peripheral Neuropathy in Thermal Videos of the Plantar Foot,
Southwest24(25-28)
IEEE DOI 2503
Support vector machines, Temperature sensors, Vibrations, Temperature measurement, Optical fiber sensors, Vectors, Diabetes, thermal imaging BibRef

Mok, D.[Dahyun], Bum, J.[Junghyun], Tai, L.D.[Le Duc], Choo, H.[Hyunseung],
Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification,
ACCV24(II: 39-53).
Springer DOI 2412
BibRef

Gaikwad, A.[Avinash], Gautam, A.[Anjali],
Segmentation of Hard Exudates and Hemorrhages from Diabetic Retinopathy Images Using Residual U-Net with Squeeze and Excite Blocks,
ICIP24(3064-3069)
IEEE DOI 2411
Training, Deep learning, Image segmentation, Diabetic retinopathy, Neural networks, Eye diseases, Boosting, Diabetic Retinopathy, Deep Learning BibRef

Cherukuri, T.K.[Teja Krishna], Shaik, N.S.[Nagur Shareef], Ye, D.H.[Dong Hye],
Guided Context Gating: Learning To Leverage Salient Lesions in Retinal Fundus Images,
ICIP24(3098-3104)
IEEE DOI 2411
Pathology, Diabetic retinopathy, Visualization, Attention mechanisms, Correlation, Accuracy, Retina, Diabetic Retinopathy BibRef

Zhu, W.H.[Wen-Hui], Qiu, P.[Peijie], Chen, X.[Xiwen], Li, X.[Xin], Lepore, N.[Natasha], Dumitrascu, O.M.[Oana M.], Wang, Y.L.[Ya-Lin],
nnMobileNet: Rethinking CNN for Retinopathy Research,
EnhanceMedIm24(2285-2294)
IEEE DOI Code:
WWW Link. 2410
Location awareness, Computational modeling, Transformers, Retina, Trajectory, Retinal Diseases, Fundus image, MobileNet BibRef

Poles, I.[Isabella], d'Arnese, E.[Eleonora], Cellamare, L.G.[Luca G.], Santambrogio, M.D.[Marco D.], Yi, D.[Darvin],
Repurposing the Image Generative Potential: Exploiting GANs to Grade Diabetic Retinopathy,
EnhanceMedIm24(2305-2314)
IEEE DOI 2410
Measurement, Training, Diabetic retinopathy, Visualization, Accuracy, Decision making, Generative adversarial networks, Diabetic Retinopathy Classification BibRef

Jwad, F.J.[Fatima Jabbar], Abdulmunem, A.A.[Ashwan A.],
Diabetic Retinopathy Identification Depend On Deep Learning Techniques: A comparative Study,
ISCV24(1-6)
IEEE DOI 2408
Deep learning, Training, Diabetic retinopathy, Reviews, Sociology, Memory management, Medical services, Diabetic retinopathy, Retina BibRef

Oulhadj, M.[Mohammed], Riffi, J.[Jamal], Khodriss, C.[Chaimae], Mahraz, A.M.[Adnane Mohamed], Yahyaouy, A.[Ali], Abdellaoui, M.[Meriem], 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 BibRef

Galappaththige, C.J.[Chamuditha Jayanga], Kuruppu, G.[Gayal], Khan, M.H.[Muhammad Haris],
Generalizing to Unseen Domains in Diabetic Retinopathy Classification,
WACV24(7670-7680)
IEEE DOI 2404
Deep learning, Diabetic retinopathy, Adaptation models, Visual impairment, Predictive models, Transformers, Calibration, and algorithms BibRef

ElHabebe, M.[Mohamed], ElKordi, S.[Shereen], ElDin, A.G.[Ahmed Gamal], Adly, N.[Noha], Torki, M.[Marwan], Elmassry, A.[Ahmed], 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 BibRef

Castillo-Munguia, A.[Agustin], Benitez-Garcia, G.[Gibran], Olivares-Mercado, J.[Jesus], Takahashi, H.[Hiroki],
Diabetic Retinopathy Grading based on a Sparse Network Fusion of Heterogeneous ConvNeXt Models with Category Attention,
MVA23(1-5)
DOI Link 2403
Diabetic retinopathy, Visualization, Machine vision, Feature extraction, Retina, Eye diseases, Convolutional neural networks BibRef

Pranay, G.[Gaddam], Misra, O.[Oshin], Mishra, S.[Surabhi], Rathore, S.S.[Santosh Singh], Bhattacharya, M.[Mahua],
Detection Analysis of Diabetic Retinopathy Using Modified DensNet-121,
ICCVMI23(1-6)
IEEE DOI 2403
Deep learning, Diabetic retinopathy, Computational modeling, Telemedicine, Blindness, Robustness, Data Pre-pocessing BibRef

Panagiotou, M.[Maria], Papathanail, I.[Ioannis], Rahman, L.A.[Lubnaa Abdur], Brigato, L.[Lorenzo], Bez, N.S.[Natalie S.], 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 Insulin Adjustment in Type 1 Diabetes Self-management,
CAIP23(II:77-86).
Springer DOI 2312
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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 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, 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
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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
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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
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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
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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
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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
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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
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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
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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