Abstract
These days one of the major causes of partial or complete blindness that has affected a majority of people all around the world is glaucoma. Glaucoma is caused as a result of increased fluid pressure inside the optic nerves called intra ocular pressure. A real time cloud-based framework for screening the glaucoma suspect’s retinal fundus images as received by the people on the public cloud, is proposed in this paper. In the proposed framework the existence of glaucoma and analysis of the retinal fundus images is achieved by deep learning technique and convolutional neural network respectively. EfficientNet and UNet++ models are used to identify the presence of glaucoma. On comparing our framework to various state-of-the-art models and quantitative assessment are performing on various benchmark datasets like RIM-ONE and DRISHTI-GS1, it was found that the proposed framework is scalable, location independent, and easily accessible to one and all due to the cloud platform.
Similar content being viewed by others
References
Abdel-Hamid L (2020) Glaucoma detection from retinal images using statistical and textural wavelet features. J Digit Imaging 33(1):151–158
Abdullah M, Fraz MM, Barman SA (2016) Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm. PeerJ 4:e2003
Aguilar-Rivera M, Erudaitius DT, Wu VM, Tantiongloc JC, Kang DY, Coleman TP et al (2020) Smart electronic eyedrop bottle for unobtrusive monitoring of glaucoma medication adherence. Sensors 20(9):2570
Al-Bander B, Al-Nuaimy W, Al-Taee MA, Zheng Y (2017) Automated glaucoma diagnosis using deep learning approach. In: 2017 14th International multi-conference on systems, signals devices (SSD). IEEE, Marrakech, pp 207–210
Almazroa A, Alodhayb S, Raahemifar K, Lakshminarayanan V (2017) Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction. Clin Ophthalmol (Auckland, NZ) 11:841
Arnay R, Fumero F, Sigut J (2017) Ant colony optimization-based method for optic cup segmentation in retinal images. Appl Soft Comput 52:409–417
Bharkad S (2017) Automatic segmentation of optic disk in retinal images. Biomed Signal Process Control 31:483–498
Chen X, Xu Y, Yan S, Wong DW, Wong TY, Liu J (2015) Automatic feature learning for glaucoma detection based on deep learning. In: International conference on medical image computing and computer-assisted intervention 2015 Oct 5. Springer, Cham, pp 669–677
Chen X, Xu Y, Wong DW, Wong TY, Liu J (2015) Glaucoma detection based on deep convolutional neural network. In: Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE 2015 Aug 25. IEEE, Milan, pp 715–718
Chen X, Xu Y, Wong DWK, Wong TY, Liu J (2015) Glaucoma detection based on deep convolutional neural network. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Milan, pp 715–718
Chrastek R, Niemann H, Kubecka L, Jan J, Derhartunian V, Michelson G (2005) Optic nerve head segmentation in multimodal retinal images. In: Medical imaging 2005: image processing, vol 5747. International Society for Optics and Photonics, Bellingham, pp 1604–1615
Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM (2018) Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs. Sci Rep 8(1):16685
Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, Cao X (2018) Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging 37(11):2493–2501
Fumero F, Alayón S, Sanchez JL, Sigut J, Gonzalez-Hernandez M (2011) RIM-ONE: an open retinal image database for optic nerve evaluation. In: 2011 24th international symposium on computer-based medical systems (CBMS). IEEE, Bristol, pp 1–6
Gupta N, Jalal AS (2019) Integration of textual cues for fine-grained image captioning using deep CNN and LSTM. Neural Comput Appl 32:17899
Gupta N, Jalal AS (2019) A robust model for salient text detection in natural scene images using MSER feature detector and Grabcut. Multimed Tools Appl 78(8):10821–10835
Gupta N, Garg H, Agarwal R (2021) A robust framework for glaucoma detection using CLAHE and EfficientNet. Vis Comput. https://doi.org/10.1007/s00371-021-02114-5
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Piscataway, pp 770–778
Juneja M, Singh S, Agarwal N, Bali S, Gupta S, Thakur N, Jindal P (2019) Automated detection of glaucoma using deep learning convolution network (G-net). Multimed Tools Appl 79:15531
Kande GB, Subbaiah PV, Savithri TS (2008) Segmentation of exudates and optic disk in retinal images. In: Computer vision, graphics & image processing. ICVGIP’08. Sixth Indian conference on 2008 Dec 16. IEEE, Bhubaneswar, pp 535–542
Li H, Chutatape O (2003) A model-based approach for automated feature extraction in fundus images. In: Null. IEEE, Nice
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(8):1199–1206
Liu SP, Chen J (2011) Detection of the optic disc on retinal fluorescein angiograms. J Med Biol Eng 31(6):405–412
Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L (2004) Optic nerve head segmentation. IEEE Trans Med Imaging 23(2):256–264
Lu S (2011) Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans Med Imag 30(12):2126–2133
Mehdizadeh M, Dolatyar S (2009) Study of effect of adaptive histogram equalization on image quality in digital preapical image in pre apex area. J Biol Sci 4(8):922–924
Ortega M, Barreira N, Novo J, Penedo MG, Pose-Reino A, Gomez-Ulla F (2010) Sirius: a web-based system for retinal image analysis. Int J Med Inform 79(10):722–732
Owoyemi A, Owoyemi J, Osiyemi A, Boyd A (2020) Artificial intelligence for healthcare in Africa. Front Digit Health 2:6
Palaniappan K, Bunyak F, Chaurasia SS (2019) Image analysis for ophthalmology: segmentation and quantification of retinal vascular systems. In: Guidoboni G, Harris A, Sacco R (eds) Ocular fluid dynamics. Modeling and simulation in science, engineering and technology. Birkhäuser, Cham
Pallawala P, Hsu W, Lee ML, Eong KGA (2004) Automated optic disc localization and contour detection using ellipse fitting and wavelet transform. ECCV. Springer, Berlin, Heidelberg, pp 139–151
Quigley HA, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90(3):262–267
Rosenthal A, Mork P, Li MH, Stanford J, Koester D, Reynolds P (2010) Cloud computing: a new business paradigm for biomedical information sharing. J Biomed Inform 43(2):342–353
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252
Shibata N, Tanito M, Mitsuhashi K, Fujino Y, Matsuura M, Murata H, Asaoka R (2018) Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Sci Rep 8(1):14665
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Singh LK, Garg H (2019) Detection of glaucoma in retinal fundus images using fast fuzzy C means clustering approach. In: 2019 International conference on computing, communication, and intelligent systems (ICCCIS). IEEE, Greater Noida, pp 397–403
Singh LK, Garg H (2020) Automated glaucoma type identification using machine learning or deep learning techniques. In: Advancement of machine intelligence in interactive medical image analysis. Springer, Singapore, pp 241–263
Singh LK, Garg H, Khanna M, Bhadoria RS (2021) An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus. Med Biol Eng Comput 59(2):333–353
Singh LK, Garg H, Khanna M (2021) An artificial intelligence-based smart system for early glaucoma recognition using OCT images. Int J E-Health Med Commun (IJEHMC) 12(4):32–59
Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Tabish AU (2014) Drishti-gs: retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, Beijing, pp 53–56
Sivaswamy J, Krishnadas S, Chakravarty A, Joshi GD, Tabish AS (2015) A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed Imaging Data Papers 2(1):1004
Soorya M, Issac A, Dutta MK (2019) Automated framework for screening of glaucoma through cloud computing. J Med Syst 43(5):136
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Piscataway, pp 1–9
Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946
Tessier-Lavigne M (2000) Visual processing by the retina. Principles of neural science. McGraw-Hill, New York, pp 507–522
Thakur N, Juneja M (2017) Clustering based approach for segmentation of optic cup and optic disc for detection of glaucoma. Curr Med Imaging Rev 13(1):99–105
Tobin KW Jr., Chaum E, Govindasamy VP, Karnowski TP, Sezer O (2006) Characterization of the optic disc in retinal imagery using a probabilistic approach. In: Medical imaging 2006: image processing, vol 6144. International Society for Optics and Photonics, Bellingham
Tuominen VJ, Ruotoistenmäki S, Viitanen A, Jumppanen M, Isola J (2010) ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res 12(4):R56
Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans Med 21:1236–1243
Wong DWK, Liu J, Lim JH, Jia X, Yin F, Li H, Wong TY (2008) Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI. In: 2008 30th Annual international conference of the IEEE engineering in medicine and biology society. IEEE, Vancouver, pp 2266–2269
Yin F, Wong DWK, Quan Y, Yow AP, Tan NM, Gopalakrishnan K et al (2015) A cloud-based system for automatic glaucoma screening. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Milan, pp 1596–1599
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Cham, pp 3–11
Zhu X, Rangayyan RM, Ells AL (2010) Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles. J Digit Imaging 23(3):332–341
Zilly JG, Buhmann JM, Mahapatra D (2015) Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 136–143
Zilly J, Buhmann JM, Mahapatra D (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph 55:28–41
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Garg, H., Gupta, N., Agrawal, R. et al. A real time cloud-based framework for glaucoma screening using EfficientNet. Multimed Tools Appl 81, 34737–34758 (2022). https://doi.org/10.1007/s11042-021-11559-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11559-8