Analysis of Deep Learning Techniques for Prediction of Eye Diseases: A Systematic Review

Archives of Computational Methods in Engineering - Trang 1-34 - 2023
Akanksha Bali1, Vibhakar Mansotra1
1Department of Computer Science and IT, University of Jammu, Jammu, India

Tóm tắt

The prediction and early diagnosis of eye diseases are critical for effective treatment and prevention of vision loss. The identification of eye diseases has recently been the subject of much advanced research. Vision problems can significantly affect a person’s quality of life, limiting their ability to perform daily activities, impacting their independence, and leading to emotional and psychological distress. Lack of timely and accurate identification of the cause of vision problems can lead to significant challenges and consequences. Delayed diagnosis prolongs the period of impaired vision and its associated negative impact on an individual’s well-being. Deep learning techniques have emerged as powerful tools for analyzing medical images, including retinal images and predicting various eye diseases. This review provides an analysis of deep learning techniques commonly used for eye disease prediction. The techniques discussed include Convolutional Neural Networks (CNNs), Transfer Learning, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Explainable Deep Learning. The application of these techniques in eye disease prediction is explored, highlighting their strengths and potential contributions. The review emphasizes the importance of collaborative efforts between deep learning researchers and healthcare professionals to ensure the safe and effective integration of these techniques. The analysis highlights the promise of deep learning in advancing the field of eye disease prediction and its potential to improve patient outcomes.

Tài liệu tham khảo

Rajagopalan N, Josephraj AN (2021) Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN. PloS one 16(7):e0254180

Chelaramani S, Gupta M, Agarwal V, Gupta P, Habash R (2021) Multi-task knowledge distillation for eye disease prediction. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 3983–3993.

Bade A, Pizzimenti JJ (2007) Interdisciplinary management of diabetic eye disease: a global approach to care. Internet J Allied Health Sci Prac 5(1):9

Lazar I, Qureshi RJ, Hajdu A (2010) A novel approach for the automatic detection of microaneurysms in retinal images. In: 2010 6th International Conference on Emerging Technologies (ICET) 193–197. IEEE.

Zhang B, Wu X, You J, Li Q, Karray F (2010) Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recogn 43(6):2237–2248

Adal KM, Sidibé D, Ali S, Chaum E, Karnowski TP, Mériaudeau F (2014) Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput Methods Programs Biomed 114(1):1

Rahim SS, Palade V, Shuttleworth J, Jayne C (2016) Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform 3:249–267

Rahim SS, Palade V, Jayne C, Holzinger A, Shuttleworth J (2015) Detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. In: Brain Informatics and Health: 8th International Conference, BIH 2015, London, UK, August 30-September 2, 2015. Proceedings 8 379–388. Springer International Publishing.

Rahim SS, Jayne C, Palade V, Shuttleworth J (2016) Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. Neural Comput Appl 27:1149–1164

Welikala RA, Dehmeshki J, Hoppe A, Tah V, Mann S, Williamson TH, Barman SA (2014) Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Programs Biomed 114(3):247–261

Weinreb RN, Aung T, Medeiros FA (2014) The pathophysiology and treatment of glaucoma: a review. JAMA 311(18):1901–1911

Issac A, Sarathi MP, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Programs Biomed 122(2):229–244

Mehta P, Petersen CA, Wen JC, Banitt MR, Chen PP, Bojikian KD, Egan C, Lee SI, Balazinska M, Lee AY, Rokem A (2021) Automated detection of glaucoma with interpretable machine learning using clinical data and multimodal retinal images. Am J Ophthalmol 231:154–169

Acharya RU, Yu W, Zhu K, Nayak J, Lim TC, Chan JY (2010) Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst 34:619–628

Hasan MK, Tanha T, Amin MR, Faruk O, Khan MM, Aljahdali S, Masud M (2021) Cataract disease detection by using transfer learning-based intelligent methods. Comput Math Methods Med. https://doi.org/10.1155/2021/7666365

Chan LKY, Lin SS, Chan F, Ng DSC (2023) Optimizing treatment for diabetic macular edema during cataract surgery. Front Endocrinol 14:1106706

Shen H, Hao H, Wei L, Wang Z (2008) An image based classification method for cataract. In: 2008 International Symposium on Computer Science and Computational Technology 1: 583–586. IEEE

Heesterbeek TJ, Lorés-Motta L, Hoyng CB, Lechanteur YT, den Hollander AI (2020) Risk factors for progression of age-related macular degeneration. Ophthalmic Physiol Opt 40(2):140–170

De Jong PT (2006) Age-related macular degeneration. N Engl J Med 355(14):1474–1485

Toto L, D’Aloisio R, Di Nicola M, Di Martino G, Di Staso S, Ciancaglini M, Tognetto D, Mastropasqua L (2017) Qualitative and quantitative assessment of vascular changes in diabetic macular edema after dexamethasone implant using optical coherence tomography angiography. Int J Mol Sci 18(6):1181

Klein R, Knudtson MD, Lee KE, Gangnon R, Klein BE (2009) The wisconsin epidemiologic study of diabetic retinopathy XXIII: the twenty-five-year incidence of macular edema in persons with type 1 diabetes. Ophthalmology 116(3):497–503

Mathers CD, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 3(11):e442

Ono K, Hiratsuka Y, Murakami A (2010) Global inequality in eye health: country-level analysis from the Global burden of disease study. Am J Public Health 100(9):1784–1788

Morone P, Camacho Cuena E, Kocur I, Banatvala N Investing in eye health: securing the support of decision-makers.

Kalra G, Kar SS, Sevgi DD, Madabhushi A, Srivastava SK, Ehlers JP (2021) Quantitative imaging biomarkers in age-related macular degeneration and diabetic eye disease: a step closer to precision medicine. J Pers Med 11(11):1161

Biswas J, Nandi K, Sridharan S, Ranjan P (2008) Ocular manifestation of storage diseases. Curr Opin Ophthalmol 19(6):507–511

Ashworth JL, Biswas S, Wraith E, Lloyd IC (2006) The ocular features of the mucopolysaccharidoses. Eye 20(5):553–563

Keane PA, Patel PJ, Liakopoulos S, Heussen FM, Sadda SR, Tufail A (2012) Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol 57(5):389–414

Dargan S, Kumar M, Ayyagari MR, Kumar G (2020) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27:1071–1092

Ahmed Z, Mohamed K, Zeeshan S, Dong X (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database. https://doi.org/10.1093/database/baaa010

Pirbhai A, Sheidow T, Hooper P (2005) Prospective evaluation of digital non-stereo color fundus photography as a screening tool in age-related macular degeneration. Am J Ophthalmol 139(3):455–461

Wong TY, Klein R, Klein BE, Tielsch JM, Hubbard L, Nieto FJ (2001) Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv Ophthalmol 46(1):59–80

Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158–164

Karthiyayini R, Shenbagavadivu N (2021) Retinal image analysis for ocular disease prediction using rule mining algorithms. Interdiscip Sci: Comput Life Sci 13:451–462

Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A (2021) Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors 21(16):5283

Kalpana Devi M, Mary Shanthi Rani M (2022) Classification of Diabetic Retinopathy Using Ensemble of Machine Learning Classifiers with IDRiD Dataset. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2021 291–303. Singapore: Springer Singapore.

Roque PJ, Hatch N, Barr L, Wu TS (2014) Bedside ocular ultrasound. Crit Care Clin 30(2):227–241

Kumar S, Pathak S, Kumar B (2019) Automated detection of eye related diseases using digital image processing. In: Singh AK, Mohan A (eds) Handbook of multimedia information security: techniques and applications. Springer International Publishing, Cham, pp 513–44

Drexler W, Fujimoto JG (eds) (2015) Optical coherence tomography: technology and applications. Springer, Berlin

Fujimoto JG (2001) Optical coherence tomography. Comptes Rendus de l'Académie des Sciences-Series IV-Physics 2(8): 1099-111

Khanamiri HN, Nakatsuka A, El-Annan J (2017) Smartphone fundus photography. JoVE: J Vis Exp 6(125):e55958

Bruce BB, Lamirel C, Wright DW, Ward A, Heilpern KL, Biousse V, Newman NJ (2011) Nonmydriatic ocular fundus photography in the emergency department. N Engl J Med 364(4):387–389

Panwar N, Huang P, Lee J, Keane PA, Chuan TS, Richhariya A, Teoh S, Lim TH, Agrawal R (2016) Fundus photography in the 21st century—a review of recent technological advances and their implications for worldwide healthcare. Telemed e-Health 22(3):198–208

Russo A, Morescalchi F, Costagliola C, Delcassi L, Semeraro F (2015) A novel device to exploit the smartphone camera for fundus photography. J Ophthalmol. https://doi.org/10.1155/2015/823139

Larsson E, Eriksson U, Alm A (2011) Retinal nerve fibre layer thickness in full-term children assessed with Heidelberg retinal tomography and optical coherence tomography: normal values and interocular asymmetry. Acta Ophthalmol 89(2):151–158

Ullah N, Mohmand MI, Ullah K, Gismalla MS, Ali L, Khan SU, Ullah N (2022) Diabetic retinopathy detection using genetic algorithm-based CNN features and error correction output code SVM framework classification model. Wirel Commun Mobile Comput. https://doi.org/10.1155/2022/7095528

Bulsara V, Bothra S, Sharma P, Rao KM (2011) Low cost medical image processing system for rural/semi urban healthcare. In 2011 IEEE Recent Advances in Intelligent Computational Systems 724–728. IEEE.

Han ZK, Xing H, Yang B, Hong CY (2022) A few-shot learning-based eye diseases screening method. Eur Rev Med Pharmacol Sci 26(23):8660–8674

Abbas Q, Fondon I, Sarmiento A, Jiménez S, Alemany P (2017) Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Compu 55:1959–1974

Carrera EV, González A, Carrera R (2017) Automated detection of diabetic retinopathy using SVM. In: 2017 IEEE XXIV international conference on electronics, electrical engineering and computing (INTERCON) 1–4. IEEE

Kaur S, Singh D (2018) Early detection and classification of diabetic retinopathy using empirical transform and SVM. In: Hemanth DJ, Smys S (eds) Computational vision and bio inspired computing. Springer International Publishing, Cham, pp 1072–1083

Eswari MS, Balamurali S (2021) An intelligent machine learning support system for glaucoma prediction among diabetic patients. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 447–449. IEEE

Sharma A, Agrawal M, Roy SD, Gupta V (2020) Automatic glaucoma diagnosis in digital fundus images using deep CNNs. In: Jain S, Sood M, Paul S (eds) Advances in computational intelligence techniques. Singapore, Springer Singapore, pp 37–52

Garcia GG, Nitta K, Lavieri MS, Andrews C, Liu X, Lobaza E, Van Oyen MP, Sugiyama K, Stein JD (2019) Using Kalman filtering to forecast disease trajectory for patients with normal tension glaucoma. Am J Ophthalmol 199:111–119

Guo L, Yang JJ, Peng L, Li J, Liang Q (2015) A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Comput Ind 69:72–80

Zheng J, Guo L, Peng L, Li J, Yang J, Liang Q (2014) Fundus image based cataract classification. In2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings 90–94. IEEE

Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13):5200–5206

Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PloS one 12(11):e0187336

Ting DS, Cheung CY, Lim G, Tan GS, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223

Gondal WM, Köhler JM, Grzeszick R, Fink GA, Hirsch M (2017) Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In2017 IEEE International Conference on Image Processing (ICIP) 2069–2073. IEEE.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

Guo Y, Hormel TT, Gao L, You Q, Wang B, Flaxel CJ, Bailey ST, Choi D, Huang D, Hwang TS, Jia Y (2021) Quantification of nonperfusion area in montaged widefield OCT angiography using deep learning in diabetic retinopathy. Ophthalmol Sci 1(2):100027

Daanouni O, Cherradi B, Tmiri A (2021) Self-attention mechanism for diabetic retinopathy detection. InEmerging Trends in ICT for Sustainable Development: The Proceedings of NICE2020 International Conference 79–88. Springer International Publishing

Li G, Zheng S, Li X (2018) Exudate detection in fundus images via convolutional neural network. In: Digital TV and Wireless Multimedia Communication: 14th International Forum, IFTC 2017, Shanghai, China, November 8–9, 2017, Revised Selected Papers 14 193–202. Springer Singapore

Perdomo O, Arevalo J, González FA (2017) Convolutional network to detect exudates in eye fundus images of diabetic subjects. In: 12th International Symposium on Medical Information Processing and Analysis 10160: 235–240

Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H (2017) Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PloS one 12(6):e0179790

Christopher M, Bowd C, Belghith A, Goldbaum MH, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM, Zangwill LM (2020) Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps. Ophthalmology 127(3):346–356

Shoukat A, Akbar S (2021) Artificial intelligence techniques for glaucoma detection through retinal images: State of the art. In: Goyal LM, Saba T, Rehman A, Larabi-Marie-Sainte S (eds) Artificial Intelligence and Internet of Things. CRC Press, Boca Raton, pp 209–40

Muhammad H, Fuchs TJ, De Cuir N, De Moraes CG, Blumberg DM, Liebmann JM, Ritch R, Hood DC (2017) Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma 26(12):1086

Sudhan MB, Sinthuja M, Pravinth Raja S, Amutharaj J, Charlyn Pushpa Latha G, Sheeba Rachel S, Anitha T, Rajendran T, Waji YA (2022) Segmentation and classification of glaucoma using U-net with deep learning model. J Healthc Eng. https://doi.org/10.1155/2022/1601354

Berchuck SI, Mukherjee S, Medeiros FA (2019) Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder. Sci Rep 9(1):18113

Asano S, Asaoka R, Murata H, Hashimoto Y, Miki A, Mori K, Ikeda Y, Kanamoto T, Yamagami J, Inoue K (2021) Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images. Sci Rep 11(1):2214

Gómez-Valverde JJ, Antón A, Fatti G, Liefers B, Herranz A, Santos A, Sánchez CI, Ledesma-Carbayo MJ (2019) Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 10(2):892–913

Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K (2019) Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep 9(1):10750

Al-Bander B, Al-Nuaimy W, Al-Taee MA, Williams BM, Zheng Y (2016) Diabetic macular edema grading based on deep neural networks. In: Ophthalmic Medical Image Analysis International Workshop 3(2016). University of Iowa.

Prentašić P, Lončarić S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Programs Biomed 137:281–292

Tan JH, Fujita H, Sivaprasad S, Bhandary SV, Rao AK, Chua KC, Acharya UR (2017) Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci 420:66–76

Adly MM, Ghoneim AS, Youssif AA (2019) On the grading of diabetic retinopathies using a binary-tree-based multiclass classifier of cnns. International Journal of Computer Science and Information Security (IJCSIS) 17(1).

Pratap T, Kokil P (2019) Computer-aided diagnosis of cataract using deep transfer learning. Biomed Signal Process Control 53:101533

Sarki R, Ahmed K, Wang H, Zhang Y (2020) Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst 8(1):32

Ram A, Reyes-Aldasoro CC (2020) The relationship between Fully Connected Layers and number of classes for the analysis of retinal images. arXiv preprint arXiv:2004.03624.

Wang J, Yang L, Huo Z, He W, Luo J (2020) Multi-label classification of fundus images with efficientnet. IEEE Access 8:212499–212508

Iyer SS, Gensollen T, Gandhi A, Oh SF, Neves JF, Collin F, Lavin R, Serra C, Glickman J, de Silva PS, Sartor RB (2018) Dietary and microbial oxazoles induce intestinal inflammation by modulating aryl hydrocarbon receptor responses. Cell 173(5):1123–1134

Das V, Dandapat S, Bora PK (2019) Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed Signal Process Control 54:101605

Bhowmik A, Kumar S, Bhat N (2019) Eye disease prediction from optical coherence tomography images with transfer learning. InEngineering Applications of Neural Networks: 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24–26, 2019, Proceedings 104–114. Springer International Publishing

Li F, Chen H, Liu Z, Zhang X, Wu Z (2019) Fully automated detection of retinal disorders by image-based deep learning. Graefes Arch Clin Exp Ophthalmol 257:495–505

Alqudah AM (2020) AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Compu 58:41–53

Ghosh R, Ghosh K, Maitra S (2017) Automatic detection and classification of diabetic retinopathy stages using CNN. In 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) 550–554. IEEE

Raju M, Pagidimarri V, Barreto R, Kadam A, Kasivajjala V, Aswath A (2017) Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. In: MEDINFO 2017: Precision Healthcare through Informatics 559–563. IOS Press

Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282

Taj IA, Sajid M, Karimov KS (2021) An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography. Math Biosci Eng 18(5):5321–5347

Peng Y, Dharssi S, Chen Q, Keenan TD, Agrón E, Wong WT, Chew EY, Lu Z (2019) DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4):565–575

Luo X, Pu Z, Xu Y, Wong WK, Su J, Dou X, Ye B, Hu J, Mou L (2021) MVDRNet: multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms. Pattern Recogn 120:108104

Lyu X, Li H, Zhen Y, Ji X, Zhang S (2017) Deep tessellated retinal image detection using Convolutional Neural Networks. In2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 676–680. IEEE

Ardiyanto I, Nugroho HA, Buana RL (2017) Deep learning-based diabetic retinopathy assessment on embedded system. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1760–1763. IEEE

Orlando JI, Prokofyeva E, Del Fresno M, Blaschko MB (2018) An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Programs Biomed 153:115–127

Bali A, Mansotra V (2021) Transfer learning-based one versus rest classifier for multiclass multi-label ophthalmological disease prediction. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2021.0121269

Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 441:41–49

Serener A, Serte S (2019) Transfer learning for early and advanced glaucoma detection with convolutional neural networks. In: 2019 Medical technologies congress (TIPTEKNO) 1–4. IEEE

Naser DS, Saha G (2021) Influence of music liking on EEG based emotion recognition. Biomed Signal Process Control 64:102251

AbdelMaksoud E, Barakat S, Elmogy M (2019) A multi-label computer-aided diagnoses system for detecting and diagnosing diabetic retinopathy. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES) 368–373. IEEE

Islam MT, Imran SA, Arefeen A, Hasan M, Shahnaz C (2019) Source and camera independent ophthalmic disease recognition from fundus image using neural network. In: 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON) 59–63. IEEE

Saini M, Susan S (2022) Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets. Comput Biol Med 149:105989

Gour N, Khanna P (2021) Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomed Signal Process Control 66:102329

Juneja M, Thakur S, Uniyal A, Wani A, Thakur N, Jindal P (2022) Deep learning-based classification network for glaucoma in retinal images. Comput Electr Eng 101:108009

Shinde R (2021) Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms. Intell-Based Med 5:100038

Thakur A, Goldbaum M, Yousefi S (2020) Predicting glaucoma before onset using deep learning. Ophthalmol Glaucoma 3(4):262–268

Sandoval-Cuellar HJ, Alfonso-Francia G, Vázquez-Membrillo MA, Ramos-Arreguín JM, Tovar-Arriaga S (2021) Image-based glaucoma classification using fundus images and deep learning. Rev Mex de Ing Bioméd. https://doi.org/10.17488/rmib.42.3.2

Kim M, Han JC, Hyun SH, Janssens O, Van Hoecke S, Kee C, De Neve W (2019) Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Appl Sci 9(15):3064

Sreng S, Maneerat N, Hamamoto K, Win KY (2020) Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl Sci 10(14):4916

Tan JH, Bhandary SV, Sivaprasad S, Hagiwara Y, Bagchi A, Raghavendra U, Rao AK, Raju B, Shetty NS, Gertych A, Chua KC (2018) Age-related macular degeneration detection using deep convolutional neural network. Futur Gener Comput Syst 87:127–135

García-Floriano A, Ferreira-Santiago Á, Camacho-Nieto O, Yáñez-Márquez C (2019) A machine learning approach to medical image classification: detecting age-related macular degeneration in fundus images. Comput Electr Eng 75:218–229

Ma D, Kumar M, Khetan V, Sen P, Bhende M, Chen S, Timothy TL, Lee S, Navajas EV, Matsubara JA, Ju MJ (2022) Clinical explainable differential diagnosis of polypoidal choroidal vasculopathy and age-related macular degeneration using deep learning. Comput Biol Med 143:105319

Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T (2019) Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv Ophthalmol 64(4):498–511

Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, Podkowinski D, Gerendas BS, Langs G, Schmidt-Erfurth U (2018) Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125(4):549–558

Schmidt-Erfurth U, Vogl WD, Jampol LM, Bogunović H (2020) Application of automated quantification of fluid volumes to anti–VEGF therapy of neovascular age-related macular degeneration. Ophthalmology 127(9):1211–1219

Bali A & Mansotra V (2021, December) Deep Learning-based Techniques for the Automatic Classification of Fundus Images: A Comparative Study. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE, 351–359

Chen X, Xu Y, Yan S, Wong DW, Wong TY, Liu J (2015) Automatic feature learning for glaucoma detection based on deep learning. InMedical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 669–677. Springer International Publishing

Serte S, Serener A (2019) A generalized deep learning model for glaucoma detection. In: 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT) 1–5. IEEE

Yu S, Xiao D, Frost S, Kanagasingam Y (2019) Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graph 74:61–71

Christopher M, Nakahara K, Bowd C, Proudfoot JA, Belghith A, Goldbaum MH, Rezapour J, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM (2020) Effects of study population, labeling and training on glaucoma detection using deep learning algorithms. Trans Vis Sci Technol 9(2):27

Chai Y, Liu H, Xu J (2018) Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowl-Based Syst 161:147–156

Liao W, Zou B, Zhao R, Chen Y, He Z, Zhou M (2019) Clinical interpretable deep learning model for glaucoma diagnosis. IEEE J Biomed Health Inform 24(5):1405–1412

Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, Cha J (2022) An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors 22(2):434

Abbas Q (2017) Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2017.080606

Saxena A, Vyas A, Parashar L, Singh U (2020) A glaucoma detection using convolutional neural network. In: 2020 international conference on electronics and sustainable communication systems (ICESC) 815–820. IEEE

Suguna G, Lavanya R (2021) Performance assessment of EyeNet model in glaucoma diagnosis. Pattern Recognit Image Anal 31(2):334–344

Natarajan D, Sankaralingam E, Balraj K, Karuppusamy S (2022) A deep learning framework for glaucoma detection based on robust optic disc segmentation and transfer learning. Int J Imaging Syst Technol 32(1):230–250

Lee J, Kim YK, Park KH, Jeoung JW (2020) Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma 29(4):287–294

Diaz-Pinto A, Morales S, Naranjo V, Köhler T, Mossi JM, Navea A (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18:1–9

Juneja M, Singh S, Agarwal N, Bali S, Gupta S, Thakur N, Jindal P (2020) Automated detection of glaucoma using deep learning convolution network (G-net). Multimed Tools Appl 79:15531–15553

Veena HN, Muruganandham A, Kumaran TS (2022) A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J King Saud Univ-Comput Inf Sci 34(8):6187–6198

Fu H, Cheng J, Xu Y, Zhang C, Wong DW, Liu J, Cao X (2018) Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging 37(11):2493–2501

Gheisari S, Shariflou S, Phu J, Kennedy PJ, Agar A, Kalloniatis M, Golzan SM (2021) A combined convolutional and recurrent neural network for enhanced glaucoma detection. Sci Rep 11(1):1–1

Jumanto J, Nugraha FW, Harjoko A, Muslim MA, Alabid NN (2023) Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning. J Soft Comput Explor. https://doi.org/10.52465/joscex.v4i1.99

Almustafa KM, Sharma AK, Bhardwaj S (2023) STARC: deep learning Algorithms’ modelling for STructured analysis of retina classification. Biomed Signal Process Control 80:104357

Kumar Y, Gupta S (2023) Deep transfer learning approaches to predict glaucoma, cataract, choroidal neovascularization, diabetic macular edema, drusen and healthy eyes: an experimental review. Arch Comput Methods Eng 30(1):521–541

Sarki R, Ahmed K, Wang H, Zhang Y, Wang K (2022) Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans Scalable Inf Syst 9(4):e5–e5

Abbood SH, Hamed HNA, Rahim MSM, Rehman A, Saba T, Bahaj SA (2022) Hybrid retinal image enhancement algorithm for diabetic retinopathy diagnostic using deep learning model. IEEE Access 10:73079–73086

Butt MM, Iskandar DA, Abdelhamid SE, Latif G, Alghazo R (2022) Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics 12(7):1607

Muthukannan P (2022) Optimized convolution neural network based multiple eye disease detection. Comput Biol Med 146:105648

Sengar N, Joshi RC, Dutta MK, Burget R (2023) EyeDeep-Net: A multi-class diagnosis of retinal diseases using deep neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08249-x

Pavani PG, Biswal B, Gandhi TK (2023) Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy. Biomed Signal Process Control 86:105205

Ou X, Gao L, Quan X, Zhang H, Yang J, Li W (2022) BFENet: a two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images. Comput Methods Programs Biomed 219:106739

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