EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network

https://www.who.int/publications/i/item/world-report-on-vision, Accessed 25 Feb 2022

Jonas JB, Bourne RRA, White RA, Flaxman SR, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Price H, Wong TY, Resnikoff S, Taylor HR (2014) visual Impairment and blindness due to macular diseases globally: a systematic review and meta-analysis. Am J Ophthalmol 158:808–815. https://doi.org/10.1016/j.ajo.2014.06.012

Michaud L, Forcier P (2014) Prevalence of asymptomatic ocular conditions in subjects with refractive-based symptoms. J Optom 7:153–160. https://doi.org/10.1016/j.optom.2013.08.003

Méjécase C, Malka S, Guan Z, Slater A, Arno G, Moosajee M (2020) Practical guide to genetic screening for inherited eye diseases. Ther Adv Ophthalmol 12:251584142095459. https://doi.org/10.1177/2515841420954592

Leasher JL, Bourne RRA, Flaxman SR, Jonas JB, Keeffe J, Naidoo K, Pesudovs K, Price H, White RA, Wong TY, Resnikoff S, Taylor HR (2016) Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010. Diabetes Care 39:1643–1649. https://doi.org/10.2337/dc15-2171

Walton OB, Garoon RB, Weng CY, Gross J, Young AK, Camero KA, Jin H, Carvounis PE, Coffee RE, Chu YI (2016) Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol 134:204. https://doi.org/10.1001/jamaophthalmol.2015.5083

Riazi Esfahani H, Hajizadeh F (2021) Age-related macular degeneration (ARMD). Diagnostics in Ocular Imaging. Springer, Cham, pp 463–476. https://doi.org/10.1007/978-3-030-54863-6_17

Malick H, Din N, Rajput R, Mushtaq B (2021) Cataract surgery in patients with neovascular age related macular degeneration—examination of current practice among UK ophthalmic surgeons. Eye 35:685–686. https://doi.org/10.1038/s41433-020-0863-7

Hayreh SS (1974) Pathogenesis of cupping of the optic disc. Br J Ophthalmol 58:863–876. https://doi.org/10.1136/bjo.58.10.863

Bressler NM (2004) Age-related macular degeneration is the leading cause of blindness. JAMA 291:1900. https://doi.org/10.1001/jama.291.15.1900

Ye H, Zhang Q, Liu X, Cai X, Yu W, Yu S, Wang T, Lu W, Li X, Jin H, Hu Y, Kang X, Zhao P (2014) Prevalence of age-related macular degeneration in an elderly urban Chinese population in china: the Jiangning eye study. Investig Opthalmology Vis Sci 55:6374. https://doi.org/10.1167/iovs.14-14899

Edupuganti VG, Chawla A, Kale A (2018) Automatic optic disk and cup segmentation of fundus images using deep learning. In 2018 25th IEEE international conference on image processing (ICIP), pp 2227–2231

Dias JM, Oliveira CM, da Silva Cruz LA (2014) Retinal image quality assessment using generic image quality indicators. Inf Fusion 19:73–90. https://doi.org/10.1016/j.inffus.2012.08.001

Cui H, Shen S, Gao W, Liu H, Wang Z (2019) Efficient and robust large-scale structure-from-motion via track selection and camera prioritization. ISPRS J Photogramm Remote Sens 156:202–214. https://doi.org/10.1016/j.isprsjprs.2019.08.005

Peli E, Peli T (1989) Restoration of retinal images obtained through cataracts. IEEE Trans Med Imaging 8:401–406. https://doi.org/10.1109/42.41493

Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed 124:108–120. https://doi.org/10.1016/j.cmpb.2015.10.010

Singh A, Dutta MK, Parthasarathi M, Burget R, Riha K (2014) An efficient automatic method of Optic disc segmentation using region growing technique in retinal images. In: 2014 International conference on contemporary computing and informatics (IC3I), pp 480–484. IEEE

Issac SMA, Dutta MK (2018) An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection. Int J Med Inform 110:52–70. https://doi.org/10.1016/j.ijmedinf.2017.11.015

Soni A, Rai A (2021) Automatic cataract detection using sobel and morphological dilation operation. In: Proceedings of research and applications in artificial intelligence, pp 267–276. https://doi.org/10.1007/978-981-16-1543-6_25

Issac A, Partha Sarathi M, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Programs Biomed 122:229–244. https://doi.org/10.1016/j.cmpb.2015.08.002

Ganguly S, Ganguly S, Srivastava K, Dutta MK, Parthasarathi M, Burget R, Riha K (2014) An adaptive threshold based algorithm for detection of red lesions of diabetic retinopathy in a fundus image. In: 2014 International conference on medical imaging, m-health and emerging communication systems (MedCom) pp 91–94. https://doi.org/10.1109/MedCom.2014.7005982

Wang W (2022) Further results on mean-square exponential input-to-state stability of stochastic delayed cohen-grossberg neural networks. Neural Process Lett. https://doi.org/10.1007/s11063-022-10974-8

Huang C, Liu B, Yang H, Cao J (2022) Positive almost periodicity on SICNNs incorporating mixed delays and D operator. Nonlinear Anal Model Control 27:1–21

Long S, Chen J, Hu A, Liu H, Chen Z, Zheng D (2020) Microaneurysms detection in color fundus images using machine learning based on directional local contrast. Biomed Eng Online 19:21. https://doi.org/10.1186/s12938-020-00766-3

Rekhi RS, Issac A, Dutta MK, Travieso CM (2017) Automated classification of exudates from digital fundus images. In: 2017 International conference and workshop on bioinspired intelligence (IWOBI) pp 1–6. https://doi.org/10.1109/IWOBI.2017.7985527

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. https://doi.org/10.1016/j.compeleceng.2017.11.008

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, Yang Z, Ni B, Gao P, Wang P, Hua Y, Li M (2018) 3D Deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res 78:6881–6889. https://doi.org/10.1158/0008-5472.CAN-18-0696

Yang J, Deng H, Huang X, Ni B, Xu Y (2020) Relational learning between multiple pulmonary nodules via deep set attention transformers. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI), pp 1875–1878. https://doi.org/10.1109/ISBI45749.2020.9098722

Roy AG, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C, Navab N (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8:3627. https://doi.org/10.1364/BOE.8.003627

Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179–191. https://doi.org/10.1016/j.neucom.2018.05.011

Playout C, Duval R, Cheriet F (2019) A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans Med Imaging 38:2434–2444. https://doi.org/10.1109/TMI.2019.2906319

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402. https://doi.org/10.1001/jama.2016.17216

Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125:1199–1206. https://doi.org/10.1016/j.ophtha.2018.01.023

. Govindaiah A, Hussain MA, Smith RT, Bhuiyan A (2018) Deep convolutional neural network based screening and assessment of age-related macular degeneration from fundus images. In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018) IEEE pp 1525–1528. https://doi.org/10.1109/ISBI.2018.8363863

Song W, Cao Y, Qiao Z, Wang Q, Yang JJ (2019) An improved semi-supervised learning method on cataract fundus image classification. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC): pp 362–367. https://doi.org/10.1109/COMPSAC.2019.10233

Karri SPK, Chakraborty D, Chatterjee J (2017) Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express 8:579. https://doi.org/10.1364/BOE.8.000579

Li X, Pang T, Xiong B, Liu W, Liang P, Wang T (2017) Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1–11. https://doi.org/10.1109/CISP-BMEI.2017.8301998

Joshi RC, Dutta MK, Sikora P, Kiac M (2020) Efficient convolutional neural network based optic disc analysis using digital fundus images. In: 2020 43rd international conference on telecommunications and signal processing (TSP), IEEE pp 533–536. https://doi.org/10.1109/TSP49548.2020.9163560.

Sarki R, Ahmed K, Wang H, Zhang Y (2020) Automated detection of mild and multi-class diabetic eye diseases using deep learning. Heal Inf Sci Syst 8:32. https://doi.org/10.1007/s13755-020-00125-5

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 9:10750. https://doi.org/10.1038/s41598-019-47181-w

T. Wu, L. Liu, T. Zhang, X. Wu, Deep learning-based risk classification and auxiliary diagnosis of macular edema, Intell. Med. 6 (2022) 100053. https://doi.org/10.1016/j.ibmed.2022.100053.

Kumar S, Kumar B (2022) Automatic early glaucoma detection by extracting parapapillary atrophy and optic disc from fundus image using SVM. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11023-7

Purna Chandra Reddy V, Gurrala KK (2022) Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights. Biomed Signal Process Control 73:103439

Veena HN, Muruganandham A, Senthil Kumaran T (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 34:6187–6198. https://doi.org/10.1016/j.jksuci.2021.02.003

Li F, Wang Y, Xu T, Dong L, Yan L, Jiang M, Zhang X, Jiang H, Wu Z, Zou H (2022) Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye 36:1433–1441. https://doi.org/10.1038/s41433-021-01552-8

Pascal L, Perdomo OJ, Bost X, Huet B, Otálora S, Zuluaga MA (2022) Multi-task deep learning for glaucoma detection from color fundus images. Sci Rep 12:12361. https://doi.org/10.1038/s41598-022-16262-8

Pachade S, Porwal P, Thulkar D, Kokare M, Deshmukh G, Sahasrabuddhe V, Giancardo L, Quellec G, Mériaudeau F (2021) Retinal fundus multi-disease image dataset (RFMiD): a dataset for multi-disease detection research. Data 6:14. https://doi.org/10.3390/data6020014

Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

Agarap AF (2018) Deep learning using rectified linear units (ReLU). http://arxiv.org/abs/1803.08375

Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. http://arxiv.org/abs/1811.03378

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. http://arxiv.org/abs/1409.1556

Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386

Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. http://arxiv.org/abs/1602.07261

Yu X, Kang C, Guttery DS, Kadry S, Chen Y, Zhang Y-D (2020) ResNet-SCDA-50 for breast abnormality classification. IEEE/ACM Trans Comput Biol Bioinforma. 18:94–102. https://doi.org/10.1109/tcbb.2020.2986544

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J (2020) An image is worth 16x16 words: transformers for image recognition at scale. http://arxiv.org/abs/2010.11929