21.5.4 Glaucoma Retinopathy, Retinal Analysis Application

Lee, S.[Simon], Brady, J.M.[J. Michael],
Integrating stereo and photometric stereo to monitor the development of glaucoma,
IVC(9), No. 1, February 1991, pp. 39-44.
Elsevier DOI 0401
BibRef
Earlier: BMVC90(xx-yy).
PDF File. 9009
BibRef

Corona, E., Mitra, S., Wilson, M., Krile, T., Kwon, Y.H., Soliz, P.,
Digital stereo image analyzer for generating automated 3-D measures of optic disc deformation in glaucoma,
MedImg(21), No. 10, October 2002, pp. 1244-1253.
IEEE Top Reference. 0301
BibRef

Grau, V., Downs, J.C., Burgoyne, C.F.,
Segmentation of trabeculated structures using an anisotropic Markov random field: application to the study of the optic nerve head in glaucoma,
MedImg(25), No. 3, March 2006, pp. 245-255.
IEEE DOI 0604
BibRef

Vermeer, K.A., Vos, F.M., Lo, B., Zhou, Q., Lemij, H.G., Vossepoel, A.M., van Vliet, L.J.,
Modeling of Scanning Laser Polarimetry Images of the Human Retina for Progression Detection of Glaucoma,
MedImg(25), No. 5, May 2006, pp. 517-528.
IEEE DOI 0605
BibRef

Xu, J.[Juan], Chutatape, O.[Opas], Sung, E.[Eric], Zheng, C.[Ce], Kuan, P.C.T.[Paul Chew Tec],
Optic disk feature extraction via modified deformable model technique for glaucoma analysis,
PR(40), No. 7, July 2007, pp. 2063-2076.
Elsevier DOI 0704
Boundary detection; Optic disk; Cup; Snake; Deformable model; Fundus image BibRef

Hood, D.C.[Donald C.],
Relating retinal nerve fiber thickness to behavioral sensitivity in patients with glaucoma: application of a linear model,
JOSA-A(24), No. 5, May 2007, pp. 1426-1430.
WWW Link. 0801
BibRef

Joshi, G.D.[Gopal Datt], Sivaswamy, J.[Jayanthi], Krishnadas, S.R.,
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment,
MedImg(30), No. 6, June 2011, pp. 1192-1205.
IEEE DOI 1101
BibRef
Earlier: A1, A2, Only:
Colour Retinal Image Enhancement Based on Domain Knowledge,
ICCVGIP08(591-598).
IEEE DOI 0812
BibRef

Joshi, G.D.[Gopal Datt], Gautam, R.[Rohit], Sivaswamy, J.[Jayanthi], Krishnadas, S.R.,
Robust optic disk segmentation from colour retinal images,
ICCVGIP10(330-336).
DOI Link 1111
BibRef

Singh, J.[Jeetinder], Joshi, G.D.[Gopal Datt], Sivaswamy, J.[Jayanthi],
Appearance-based object detection in colour retinal images,
ICIP08(1432-1435).
IEEE DOI 0810
BibRef

Nath, M.K.[Malaya Kumar], Dandapat, S.[Samarendra],
Differential entropy in wavelet sub-band for assessment of glaucoma,
IJIST(22), No. 3, September 2012, pp. 161-165.
DOI Link 1208
BibRef

Cheng, J.[Jun], Liu, J.[Jiang], Xu, Y.[Yanwu], Yin, F.S.[Feng-Shou], Wong, D.W.K.[Damon Wing Kee], Tan, N.M.[Ngan-Meng], Tao, D., Cheng, C.Y., Aung, T., Wong, T.Y.,
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening,
MedImg(32), No. 6, 2013, pp. 1019-1032.
IEEE DOI Deformable models; Glaucoma screening; optic disc segmentation 1307
BibRef

Cheng, J.[Jun], Liu, J.[Jiang], Xu, Y.[Yanwu], Yin, F.S.[Feng-Shou], Wong, D.W.K.[Damon Wing Kee], Tan, N.M.[Ngan-Meng],
Superpixel Classification Based Optic Disc Segmentation,
ACCV12(II:293-304).
Springer DOI 1304
BibRef

Xu, Y.[Yanwu], Liu, J.[Jiang], Cheng, J.[Jun], Yin, F.[Fengshou], Tan, N.M.[Ngan Meng], Wong, D.W.K.[Damon Wing Kee], Cheng, C.Y.[Ching Yu], Tham, Y.C.[Yih Chung], Wong, T.Y.[Tien Yin],
Efficient optic cup localization based on superpixel classification for glaucoma diagnosis in digital fundus images,
ICPR12(49-52).
WWW Link. 1302
BibRef

Septiarini, A.[Anindita], Harjoko, A.[Agus], Pulungan, R.[Reza], Ekantini, R.[Retno],
Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation,
SIViP(11), No. 5, July 2017, pp. 945-952.
Springer DOI 1706
BibRef

Khalil, T.[Tehmina], Akram, M.U.[Muhammad Usman], Khalid, S.[Samina], Jameel, A.[Amina],
Improved automated detection of glaucoma from fundus image using hybrid structural and textural features,
IET-IPR(11), No. 9, September 2017, pp. 693-700.
DOI Link 1709
BibRef

Fu, H., Xu, Y., Lin, S., Zhang, X., Wong, D.W.K., Liu, J., Frangi, A.F., Baskaran, M., Aung, T.,
Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT,
MedImg(36), No. 9, September 2017, pp. 1930-1938.
IEEE DOI 1709
Cirrus high-definition-OCT data set, ocular structure, optical coherence tomography, Iris, anterior chamber angle, segmentation BibRef

Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D.W.K., Liu, J., Cao, X.,
Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,
MedImg(37), No. 11, November 2018, pp. 2493-2501.
IEEE DOI 1811
Streaming media, Image segmentation, Optical imaging, Biomedical optical imaging, Visualization, neural network BibRef

Kirar, B.S.[Bhupendra Singh], Agrawal, D.K.[Dheeraj Kumar],
Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images,
IET-IPR(13), No. 1, January 2019, pp. 73-82.
DOI Link 1812
BibRef

Ramzan, A.[Aneeqa], Akram, M.U.[Muhammad Usman], Shaukat, A.[Arslan], Khawaja, S.G.[Sajid Gul], Yasin, U.U.[Ubaid Ullah], Butt, W.H.[Wasi Haider],
Automated glaucoma detection using retinal layers segmentation and optic cup-to-disc ratio in optical coherence tomography images,
IET-IPR(13), No. 3, February 2019, pp. 409-420.
DOI Link 1903
BibRef

Diaz-Pinto, A.[Andres], Colomer, A.[Adrián], Naranjo, V.[Valery], Morales, S.[Sandra], Xu, Y.[Yanwu], Frangi, A.F.[Alejandro F.],
Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment,
MedImg(38), No. 9, September 2019, pp. 2211-2218.
IEEE DOI 1909
Optical imaging, Biomedical optical imaging, Retina, Databases, Semisupervised learning, Synthesizers, medical imaging BibRef

Xie, Y.P.[Ying-Peng], Wan, Q.W.[Qi-Wei], Xie, H.[Hai], Xu, Y.[Yanwu], Wang, T.F.[Tian-Fu], Wang, S.Q.[Shu-Qiang], Lei, B.Y.[Bai-Ying],
Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning,
MedImg(42), No. 9, September 2023, pp. 2714-2725.
IEEE DOI 2310
BibRef

Xie, Y.P.[Ying-Peng], Wan, Q.W.[Qi-Wei], Xie, H.[Hai], Lei, B.Y.[Bai-Ying], Tan, E.L.[Ee-Leng], Xu, Y.[Yanwu],
Semi-Supervised GANs with Complementary Generator Pair for Retinopathy Screening,
ICPR21(4821-4828)
IEEE DOI 2105
Deep learning, Retinopathy, Semisupervised learning, Feature extraction, Retina, Cameras, Generators BibRef

Claro, M.[Maíla], Veras, R.[Rodrigo], Santana, A.[André], Araújo, F.[Flávio], Silva, R.[Romuere], Almeida, J.[João], Leite, D.[Daniel],
An hybrid feature space from texture information and transfer learning for glaucoma classification,
JVCIR(64), 2019, pp. 102597.
Elsevier DOI 1911
Glaucoma detection, Feature selection, Pre-trained CNNs, Transfer learning BibRef

Zhao, X.[Xin], Guo, F.[Fan], Mai, Y.X.[Yu-Xiang], Tang, J.[Jin], Duan, X.C.[Xuan-Chu], Zou, B.J.[Bei-Ji], Jiang, L.Z.[Ling-Zi],
Glaucoma screening pipeline based on clinical measurements and hidden features,
IET-IPR(13), No. 12, October 2019, pp. 2213-2223.
DOI Link 1911
BibRef

Agrawal, D.K.[Dheeraj Kumar], Kirar, B.S.[Bhupendra Singh], Pachori, R.B.[Ram Bilas],
Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images,
IET-IPR(13), No. 13, November 2019, pp. 2401-2408.
DOI Link 1911
BibRef

Garcia-Porta, N.[Nery], Gantes-Nunez, F.J.[Francisco Javier], Tabernero, J.[Juan], Pardhan, S.[Shahina],
Characterization of the ocular surface temperature dynamics in glaucoma subjects using long-wave infrared thermal imaging,
JOSA-A(36), No. 6, June 2019, pp. 1015-1021.
DOI Link 1912
BibRef
And: Correction (funding section): JOSA-A(36), No. 9, September 2019, pp. 1584-1584.
DOI Link 1912
Eyes, Glaucoma, Optic nerve, Refractive surgery, Spatial resolution, Thermal infrared imaging BibRef

Li, L.[Liu], Xu, M.[Mai], Liu, H.R.[Han-Ruo], Li, Y.[Yang], Wang, X.F.[Xiao-Fei], Jiang, L.[Lai], Wang, Z.L.[Zu-Lin], Fan, X.[Xiang], Wang, N.L.[Ning-Li],
A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection,
MedImg(39), No. 2, February 2020, pp. 413-424.
IEEE DOI 2002
Databases, Feature extraction, Optical imaging, Biomedical optical imaging, Pathology, Deep learning, weakly supervised BibRef

Li, L.[Liu], Xu, M.[Mai], Wang, X.F.[Xiao-Fei], Jiang, L.[Lai], Liu, H.R.[Han-Ruo],
Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model,
CVPR19(10563-10572).
IEEE DOI 2002
BibRef

Juneja, M.[Mamta], Thakur, S.[Sarthak], Wani, A.[Anuj], Uniyal, A.[Archit], Thakur, N.[Niharika], Jindal, P.[Prashant],
DC-Gnet for detection of glaucoma in retinal fundus imaging,
MVA(31), No. 5, July 2020, pp. Article34.
Springer DOI 2006
BibRef

Juneja, M.[Mamta], Thakur, N.[Niharika], Thakur, S.[Sarthak], Uniyal, A.[Archit], Wani, A.[Anuj], Jindal, P.[Prashant],
GC-NET for classification of glaucoma in the retinal fundus image,
MVA(31), No. 5, July 2020, pp. Article38.
Springer DOI 2006
BibRef

Gupta, K., Thakur, A., Goldbaum, M., Yousefi, S.,
Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma,
Precognition20(4393-4401)
IEEE DOI 2008
Visualization, Diseases, Machine learning, Dictionaries, Data models, Neural networks BibRef

Gour, N.[Neha], Khanna, P.[Pritee],
Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors,
PRL(137), 2020, pp. 3-11.
Elsevier DOI 2008
Glaucoma classification, GIST features, PHOG features, Automated disease diagnosis BibRef

Ajesh, F., Ravi, R.,
Hybrid features and optimization-driven recurrent neural network for glaucoma detection,
IJIST(30), No. 4, 2020, pp. 1143-1161.
DOI Link 2011
glaucoma detection, optic discs, Renyi entropy, segmentation, sparking process BibRef

Parashar, D., Agrawal, D.K.,
Automatic Classification of Glaucoma Stages Using Two-Dimensional Tensor Empirical Wavelet Transform,
SPLetters(28), 2021, pp. 66-70.
IEEE DOI 2101
Feature extraction, Databases, Transforms, Filter banks, Classification algorithms, Signal processing algorithms, multiclass classification BibRef

Elangovan, P.[Poonguzhali], Nath, M.K.[Malaya Kumar],
Glaucoma assessment from color fundus images using convolutional neural network,
IJIST(31), No. 2, 2021, pp. 955-971.
DOI Link 2105
convolutional neural network, deep learning, fundus image, glaucoma BibRef

Elangovan, P.[Poonguzhali], Nath, M.K.[Malaya Kumar],
En-ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks,
IJIST(32), No. 6, 2022, pp. 2034-2048.
DOI Link 2212
ensemble learning, fundus image, glaucoma, pre-trained model, transfer learning BibRef

Serte, S.[Sertan], Serener, A.[Ali],
Graph-based saliency and ensembles of convolutional neural networks for glaucoma detection,
IET-IPR(15), No. 3, 2021, pp. 797-804.
DOI Link 2106
BibRef

Khalil, T.[Tehmina], Akram, M.U.[Muhammad Usman], Khalid, S.[Samina], Dar, S.H.[Saadat Hanif], Ali, N.[Nouman],
A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage,
IJIST(31), No. 3, 2021, pp. 1155-1173.
DOI Link 2108
glaucoma diagnosis, image processing, machine learning, opthalmic imaging technologies BibRef

Song, D.P.[Di-Ping], Fu, B.[Bin], Li, F.[Fei], Xiong, J.[Jian], He, J.J.[Jun-Jun], Zhang, X.[Xiulan], Qiao, Y.[Yu],
Deep Relation Transformer for Diagnosing Glaucoma With Optical Coherence Tomography and Visual Field Function,
MedImg(40), No. 9, September 2021, pp. 2392-2402.
IEEE DOI 2109
Visualization, Feature extraction, Optical imaging, Retina, Cognition, Optical sensors, Optical attenuators, interaction transformer mechanism BibRef

Gupta, K.[Krati], Goldbaum, M.[Michael], Yousefi, S.[Siamak],
Glaucoma Precognition Based on Confocal Scanning Laser Ophthalmoscopy Images of the Optic Disc Using Convolutional Neural Network,
Precognition21(2259-2267)
IEEE DOI 2109
Optical design, Optical computing, Predictive models, Optical fiber networks, Feature extraction, Optical imaging, Optical receivers BibRef

Natarajan, D.[Deepa], Sankaralingam, E.[Esakkirajan], Balraj, K.[Keerthiveena], Karuppusamy, S.[Selvakumar],
A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning,
IJIST(32), No. 1, 2022, pp. 230-250.
DOI Link 2201
glaucoma, GMM super pixel, SqueezeNet, transfer learning, UNet BibRef

Abdel-Hamid, L.[Lamiaa],
TWEEC: Computer-aided glaucoma diagnosis from retinal images using deep learning techniques,
IJIST(32), No. 1, 2022, pp. 387-401.
DOI Link 2201
computer-aided glaucoma diagnosis, convolutional neural networks, deep learning, retinal images, wavelet transform BibRef

Patel, R.K.[Rajneesh Kumar], Kashyap, M.[Manish],
Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features,
IJIST(33), No. 1, 2023, pp. 246-261.
DOI Link 2301
BPS, glaucoma, LBP, LS-SVM, medical imaging, PCA BibRef

Elmoufidi, A.[Abdelali], Skouta, A.[Ayoub], Jai-Andaloussi, S.[Said], Ouchetto, O.[Ouail],
CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images,
IJIG(23), No. 1 2023, pp. 2350012.
DOI Link 2302
BibRef

Hu, X.Y.[Xiao-Yan], Zhang, L.X.[Ling-Xiao], Gao, L.[Lin], Dai, W.W.[Wei-Wei], Han, X.G.[Xiao-Guang], Lai, Y.K.[Yu-Kun], Chen, Y.Q.[Yi-Qiang],
GLIM-Net: Chronic Glaucoma Forecast Transformer for Irregularly Sampled Sequential Fundus Images,
MedImg(42), No. 6, June 2023, pp. 1875-1884.
IEEE DOI 2306
Transformers, Feature extraction, Predictive models, Image segmentation, Deep learning, Biomedical imaging, fundus image BibRef

Fan, R.[Rui], Bowd, C.[Christopher], Brye, N.[Nicole], Christopher, M.[Mark], Weinreb, R.N.[Robert N.], Kriegman, D.J.[David J.], Zangwill, L.M.[Linda M.],
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis,
MedImg(42), No. 12, December 2023, pp. 3764-3778.
IEEE DOI 2312
BibRef

de Vente, C.[Coen], Vermeer, K.A.[Koenraad A.], Jaccard, N.[Nicolas], Wang, H.[He], Sun, H.Y.[Hong-Yi], Khader, F.[Firas], Truhn, D.[Daniel], Aimyshev, T.[Temirgali], Zhanibekuly, Y.[Yerkebulan], Le, T.D.[Tien-Dung], Galdran, A.[Adrian], Ballester, M.Á.G.[Miguel Ángel González], Carneiro, G.[Gustavo], Devika, R.G., Sethumadhavan, H.P.[Hrishikesh Panikkasseril], Puthussery, D.[Densen], Liu, H.[Hong], Yang, Z.[Zekang], Kondo, S.[Satoshi], Kasai, S.[Satoshi], Wang, E.[Edward], Durvasula, A.[Ashritha], Heras, J.[Jónathan], Zapata, M.Á.[Miguel Ángel], Araújo, T.[Teresa], Aresta, G.[Guilherme], Bogunovic, H.[Hrvoje], Arikan, M.[Mustafa], Lee, Y.C.[Yeong Chan], Cho, H.B.[Hyun Bin], Choi, Y.H.[Yoon Ho], Qayyum, A.[Abdul], Razzak, I.[Imran], van Ginneken, B.[Bram], Lemij, H.G.[Hans G.], Sánchez, C.I.[Clara I.],
AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge,
MedImg(43), No. 1, January 2024, pp. 542-557.
IEEE DOI 2401
BibRef

Luo, Y.[Yan], Shi, M.[Min], Tian, Y.[Yu], Elze, T.[Tobias], Wang, M.Y.[Meng-Yu],
Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning,
ICCV23(20414-20425)
IEEE DOI Code:
WWW Link. 2401
BibRef

Kiyani, I.A.[Iqra Ashraf], Shehryar, T.[Tehmina], Khalid, S.[Samina], Jamil, U.[Uzma], Syed, A.M.[Adeel Muzaffar],
Deep learning based Glaucoma Network Classification (GNC) using retinal images,
IJIST(34), No. 2, 2024, pp. e23003.
DOI Link 2402
data augmentation, data normalization, deep learning, fine-tuning, transfer learning BibRef

Mulla, A.[Ahmed], Patel, M.[Manav], Gupta, R.K.[Rajeev Kumar],
Glaucoma Classification Using Deep Learning and Image Processing,
ICCVMI23(1-6)
IEEE DOI 2403
Glaucoma, Image segmentation, Biomedical optical imaging, Codes, Ultraviolet sources, Computational modeling, UNet BibRef

Wang, Y.[Yan], Zhen, L.[Liangli], Tan, T.E.[Tien-En], Fu, H.Z.[Hua-Zhu], Feng, Y.Q.[Yang-Qin], Wang, Z.Z.[Zi-Zhou], Xu, X.X.[Xin-Xing], Goh, R.S.M.[Rick Siow Mong], Ng, Y.[Yipin], Calhoun, C.[Claire], Tan, G.S.W.[Gavin Siew Wei], Sun, J.K.[Jennifer K.], Liu, Y.[Yong], Ting, D.S.W.[Daniel Shu Wei],
Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis,
MedImg(43), No. 5, May 2024, pp. 1945-1957.
IEEE DOI 2405
Feature extraction, Retina, Imaging, Lesions, Glaucoma, Correlation, Multimodal learning, multimodal fusion, ophthalmic image analysis BibRef

Das, D.[Dipankar], Nayak, D.R.[Deepak Ranjan], Pachori, R.B.[Ram Bilas],
AES-Net: An adapter and enhanced self-attention guided network for multi-stage glaucoma classification using fundus images,
IVC(146), 2024, pp. 105042.
Elsevier DOI 2405
BibRef
Earlier: A1, A2, Only:
GS-Net: Global Self-Attention Guided CNN for Multi-Stage Glaucoma Classification,
ICIP23(3454-3458)
IEEE DOI 2312
Multi-stage glaucoma classification, Fundus image, Spatial-adapter module, AES-Net BibRef

Luo, Y.[Yan], Tian, Y.[Yu], Shi, M.[Min], Pasquale, L.R.[Louis R.], Shen, L.Q.[Lucy Q.], Zebardast, N.[Nazlee], Elze, T.[Tobias], Wang, M.Y.[Meng-Yu],
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization,
MedImg(43), No. 7, July 2024, pp. 2623-2633.
IEEE DOI Code:
WWW Link. 2407
Glaucoma, Biomedical imaging, Data models, Finance, Medical services, Measurement, AI for eye disease screening, fairness learning BibRef

Velpula, V.K.[Vijaya Kumar], Sharma, D.[Diksha], Sharma, L.D.[Lakhan Dev], Roy, A.[Amarjit], Bhuyan, M.K.[Manas Kamal], Alfarhood, S.[Sultan], Safran, M.[Mejdl],
Glaucoma detection with explainable AI using convolutional neural networks based feature extraction and machine learning classifiers,
IET-IPR(18), No. 13, 2024, pp. 3827-3853.
DOI Link 2411
convolutional neural nets, image classification, medical image processing BibRef

Singh, M.[Mohana], Vivek, B.S.[B S], Gubbi, J.[Jayavardhana], Pal, A.[Arpan],
Prototype-based Interpretable Model for Glaucoma Detection,
DEF-AI-MIA24(5056-5065)
IEEE DOI 2410
Glaucoma, Training, Visualization, Pathology, Computational modeling, Visual impairment, Prototypes, interpretable, prototype, glaucoma detection BibRef

Brahmavar, S.B.[Shreyas Bhat], Rajesh, R.[Rohit], Dash, T.[Tirtharaj], Vig, L.[Lovekesh], Verlekar, T.T.[Tanmay Tulsidas], Hasan, M.M.[Md Mahmudul], Khan, T.[Tariq], Meijering, E.[Erik], Srinivasan, A.[Ashwin],
IKD+: Reliable Low Complexity Deep Models for Retinopathy Classification,
ICIP23(2400-2404)
IEEE DOI 2312
BibRef

Wassel, M.[Moustafa], Hamdi, A.M.[Ahmed M.], Adly, N.[Noha], Torki, M.[Marwan],
Vision Transformers Based Classification for Glaucomatous Eye Condition,
ICPR22(5082-5088)
IEEE DOI 2212
Shape, Microprocessors, Sensitivity and specificity, Transformers, Optical imaging, Optical sensors BibRef

Vaghjiani, D.[Dhaval], Saha, S.[Sajib], Connan, Y.[Yann], Frost, S.[Shaun], Kanagasingam, Y.[Yogesan],
Visualizing and Understanding Inherent Image Features in CNN-based Glaucoma Detection,
DICTA20(1-3)
IEEE DOI 2201
Visualization, Digital images, Feature extraction, Optical imaging, Convolutional neural networks, Optical devices, Diseases, Optic Disc BibRef

Zhou, B.N.[Bing-Nan], Mohammadi, F.[Farnaz], Lim, J.S.[Jung S.], Forouzesh, N.[Negin], Ghasemzadeh, H.[Hassan], Amini, N.[Navid],
Analysis of Macular Thickness Deviation Maps for Diagnosis of Glaucoma,
ISVC21(II:53-64).
Springer DOI 2112
BibRef

Urquijo, J.A.G.[Juan A. González], Fonseca, J.D.S.[Jessica D. Sánchez], López, J.M.L.[Juan M. López], Suárez, S.C.[Sandra Cancino],
Novel Features for Glaucoma Detection in Fundus Images,
MCPR21(369-378).
Springer DOI 2108
BibRef

Yang, G.[Gang], Li, F.[Fan], Ding, D.[Dayong], Wu, J.[Jun], Xu, J.[Jie],
Automatic Diagnosis of Glaucoma on Color Fundus Images Using Adaptive Mask Deep Network,
MMMod21(II:99-110).
Springer DOI 2106
BibRef

Mvoulana, A.[Amed], Kachouri, R.[Rostom], Akil, M.[Mohamed],
Fine-tuning Convolutional Neural Networks: a comprehensive guide and benchmark analysis for Glaucoma Screening,
ICPR21(6120-6127)
IEEE DOI 2105
Training, Analytical models, Transfer learning, Benchmark testing, Trademarks, Retina, Reliability engineering BibRef

Hassan, O.N.[Osama N.], Sahin, S.[Serhat], Mohammadzadeh, V.[Vahid], Yang, X.H.[Xiao-He], Amini, N.[Navid], Mylavarapu, A.[Apoorva], Martinyan, J.[Jack], Hong, T.[Tae], Mahmoudinezhad, G.[Golnoush], Rueckert, D.[Daniel], Nouri-Mahdavi, K.[Kouros], Scalzo, F.[Fabien],
Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography,
ISVC20(II:761-772).
Springer DOI 2103
BibRef

García, G.[Gabriel], del Amor, R.[Rocío], Colomer, A.[Adrián], Naranjo, V.[Valery],
Glaucoma Detection From Raw Circumpapillary OCT Images Using Fully Convolutional Neural Networks,
ICIP20(2526-2530)
IEEE DOI 2011
Databases, Training, Retina, Convolutional neural networks, Neurons, Sensitivity, Blindness, Glaucoma detection, deep learning, class activation maps BibRef

Khodris, C., Ahmed, B., Fouad, C., Meriem, A., Idriss, B.A., Tairi, H.,
Artificial intelligence in ophthalmology: the ophthalmologist's opinion,
ISCV20(1-5)
IEEE DOI 2011
artificial intelligence, biomedical optical imaging, diseases, eye, medical image processing, artificial intelligence, glaucoma BibRef

Zhao, R.C.[Rong-Chang], Chen, X.L.[Xuan-Lin], Chen, Z.L.[Zai-Liang], Li, S.[Shuo],
EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis,
ECCV20(XXI:190-205).
Springer DOI 2011
BibRef

Norouzifard, M., Nemati, A., GholamHosseini, H., Klette, R., Nouri-Mahdavi, K., Yousefi, S.,
Automated Glaucoma Diagnosis Using Deep and Transfer Learning: Proposal of a System for Clinical Testing,
IVCNZ18(1-6)
IEEE DOI 1902
Training, Deep learning, Optical imaging, Adaptive optics, Task analysis, Computational modeling, Testing, Glaucoma diagnosis, InceptionResNet-V2 BibRef

Yousefi, S., Elze, T., Pasquale, L.R., Boland, M.,
Glaucoma Monitoring Using Manifold Learning and Unsupervised Clustering,
IVCNZ18(1-6)
IEEE DOI 1902
Glaucoma Monitoring, Artificial Intelligence, Manifold Learning, Unsupervised Clustering, Machine Learning, Patterns of Visual Field Loss BibRef

Norouzifard, M., Dawda, A., Abdul-Rahman, A., GholamHosseini, H., Klette, R.,
Superpixel Segmentation Methods on Stereo Fundus Images and Disparity Map for Glaucoma Detection,
IVCNZ18(1-6)
IEEE DOI 1902
Optical imaging, Image segmentation, Clustering algorithms, Stereo vision, Image color analysis, Optical losses, superpixel segmentation BibRef

Pal, A., Moorthy, M.R., Shahina, A.,
G-Eyenet: A Convolutional Autoencoding Classifier Framework for the Detection of Glaucoma from Retinal Fundus Images,
ICIP18(2775-2779)
IEEE DOI 1809
Optical imaging, Feature extraction, Machine learning, Image reconstruction, Training, Biomedical optical imaging, Classification BibRef

Wang, L.[Lu], Kallem, V.[Vinutha], Bansal, M.[Mayank], Eledath, J.[Jayan], Sawhney, H.[Harpreet], Pearson, D.J.[Denise J.], Stone, R.A.[Richard A.],
Automatic 3D change detection for glaucoma diagnosis,
WACV14(401-408)
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Cataracts, Detection, Analysis, Surgery .