Skip to main content
SpringerLink
Log in

DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

High blood pressure and diabetes are associated with a retinal abnormality known as Hypertensive Retinopathy (HR). The severity-level and duration of hypertension are straightly related to the incidence of HR-eye disease. The HR damages the pathological lesions of eyes such as arteriolar narrowing, retinal hemorrhage, macular edema, cotton wool spots, and blood vessels. In the early stages, it is important to detect and diagnose HR to prevent eye blindness. Currently, there are few computerize systems developed to recognize HR. However, those systems focused on extracting features through hand-craft and deep-learning models (DLMs) based techniques. As a result, the complex image processing algorithms are required in case of hand-crafted features and it is difficult to define generalized features by DLMs to recognize HR. Moreover, the classification accuracy is not up-to-the-mark even though by using deep-feature techniques as observed in state-of-the-art HR diagnostics systems. To solve these problems, a novel hypertensive retinopathy (DenseHyper) system is developed to detect the HR based on a proposed trained features layer (TF-L) and dense feature transform layer (DFT-L) to the deep residual learning (DRL) methods. The DenseHyper system consists of different multilayer dense architecture by integrating of TF-L by convolutional neural network (CNN) to learn features from different lesions, and generate specialized features by DFT-L. To develop DenseHyper system, a learning based dense feature transform (DFT) approach was integrated to increase classification accuracy. Three online sources besides one private data are gathered to test and compare the DenseHyper system. To show the performance of the DenseHyper system, the statistical analysis is also performed on 4270 retinal fundus images through sensitivity (SE), specificity (SP), accuracy (ACC) and area under the receiver operating curve (AUC) metrics. The significant results were achieved compare to state-of-the-art methods. On average, the SE of 93%, SP of 95%, ACC of 95% and 0.96 of AUC values were obtained through a 10-fold cross-validation test. Experimental results confirm the applicability of the DenseHyper system to accurately diagnosis of hypertensive retinopathy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Abbas Q, Celebi ME (2019) DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimed Tools Appl 78(16):23559–23580

    Article  Google Scholar 

  2. 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 Comput 55(11):1959–1974

    Article  Google Scholar 

  3. Abbas Q, Ibrahim ME, Jaffar MA (2018) A comprehensive review of recent advances on deep vision systems. Artif Intell Rev 52:39–76

    Article  Google Scholar 

  4. Abbas Q, Ibrahim ME, Jaffar MA (2019) A comprehensive review of recent advances on deep vision systems. Artif Intell Rev 52(1):39–76

    Article  Google Scholar 

  5. Abbas Q, Ibrahim MEA, Jaffar MA (2019) Video scene analysis: an overview and challenges on deep learning algorithms. Multimed Tools Appl 77(16):20415–20453

    Article  Google Scholar 

  6. Abbasi-Sureshjani, S, Smit-Ockeloen, I, Bekkers, EJ, Dashtbozorg, B, and Romeny, BM (2016). Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores. 2016 IEEE 13th international symposium on biomedical imaging (ISBI), 189-192

  7. Agurto, C, Joshi, V, Nemeth, SC, Soliz, P, and Barriga, ES (2014). Detection of hypertensive retinopathy using vessel measurements and textural features. 2014 36th annual international conference of the IEEE engineering in medicine and biology society, 5406–5409

  8. Akagi S, Matsubara H, Nakamura K, Ito H (2018) Modern treatment to reduce pulmonary arterial pressure in pulmonary arterial hypertension. J Cardiol 72(6):466–472

    Article  Google Scholar 

  9. Akbar S, Akram MU, Sharif M, Tariq A, Khan SA (2018) Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif Intell Med 90:15–24

    Article  Google Scholar 

  10. Akbar S, Akram MU, Sharif M, Tariq A, Yasin U (2018) Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy. Comput Methods Prog Biomed 154:123–141

    Article  Google Scholar 

  11. AlBadawi, S and Fraz, FF (2018). Arterioles and Venules classification in retinal images using fully convolutional deep neural network. The 15th international conference on image analysis and recognition (ICIAR’18), 659–668

  12. Asiri N, Hussain M, Aboalsamh HA (2018) Deep learning based computer-aided diagnosis Systems for Diabetic Retinopathy: a survey. Artif Intell Med 99:101701

    Article  Google Scholar 

  13. Canziani, A, Paszke, A, and Culurciello, E (2017). An analysis of deep neural network models for practical applications. ArXiv, abs/1605.07678

  14. Cavallari, M, Stamile, C, Umeton, R, Calimeri, F, and Orzi, F (2015). Novel method for automated analysis of retinal images: results in subjects with hypertensive retinopathy and CADASIL. BioMed research international

  15. Chen C, Li S, Wang Y, Qin H, Hao A (2017) Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Trans Image Process 26(7):3156–3170

    Article  MathSciNet  MATH  Google Scholar 

  16. Chen C, Wei J, Peng C, Zhang W, Qin H (2020) Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion. IEEE Trans Image Process 29:4296–4307

    Article  Google Scholar 

  17. Deng J, Dong W, Socher R, Li L, Li K, Li F (2009) ImageNet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition 2009:248–255

  18. Gamella-Pozuelo L, Fuentes-Calvo I, Gomez-Marcos MA, Recio-Rodriguez JI, Agudo-Conde C, Fernández-Martín JL, Martínez-Salgado C (2015) Plasma cardiotrophin-1 as a marker of hypertension and diabetes-induced target organ damage and cardiovascular risk. Medicine 94:30

    Article  Google Scholar 

  19. Gao, Y, Yu, X, Wu, C, Zhou, W, Lei, X, and Zhuang, Y (2019). Automatic optic disc segmentation based on modified local image fitting model with shape prior information Journal of Healthcare Engineering, 2019

  20. García-Floriano A, Ferreira-Santiago Á, Nieto OC, Yáñez-Márquez C (2017) A machine learning approach to medical image classification: detecting age-related macular degeneration in fundus images. Comput Electr Eng 75:218–229

    Article  Google Scholar 

  21. Goswami, S, Goswami, S, and De, S (2017). Automatic measurement and analysis of vessel width in retinal fundus image. Springer 1st international conference on intelligent computing and communication, 451–458

  22. Grisan E, Foracchia M, Ruggeri A (2008) A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imaging 27:310–319

    Article  Google Scholar 

  23. He, K, Zhang, X, Ren, S, and Sun, J (2015). Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778

  24. He, K, Zhang, X, Ren, S, and Sun, J (2016). Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition, 770–778

  25. He, K, Zhang, X, Ren, S, and Sun, J (2016). Identity mappings in deep residual networks. European conference on computer vision, 630–645

  26. Holm SI, Russell G, Nourrit V, McLoughlin NP (2017) DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. Journal of Medical Imaging 4:014503

    Article  Google Scholar 

  27. Irshad S, Akram MU (2014) Classification of retinal vessels into arteries and veins for detection of hypertensive retinopathy. Cairo International Biomedical Engineering Conference (CIBEC) 2014:133–136

  28. Irshad S, Akram MU, Salman MS, Yasin U (2014) Automated detection of cotton wool spots for the diagnosis of hypertensive retinopathy. Cairo International Biomedical Engineering Conference (CIBEC) 2014:121–124

  29. Kauppi, T, Kalesnykiene, V, Kamarainen, JK, Lensu, L, Sorri, I, Raninen, A, ... & Pietilä, J (2007). The diaretdb1 diabetic retinopathy database and evaluation protocol. The 17th British Machine Vision Conference (BMVC), 1, 1–10

  30. Keshavarzian A, Sharifian S, Seyedin S (2019) Modified deep residual network architecture deployed on serverless framework of IoT platform based on human activity recognition application. Futur Gener Comput Syst 101:14–28

    Article  Google Scholar 

  31. Khitran, SA, Akram, MU, Usman, A, and Yasin, U (2014). Automated system for the detection of hypertensive retinopathy. 2014 4th international conference on image processing theory, tools and applications (IPTA), 1–6

  32. Liang G, Hong H, Xie W, Zheng L (2018) Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access 6:36188–36197

    Article  Google Scholar 

  33. Liu, S, and Deng, W (2015). Very deep convolutional neural network based image classification using small training sample size. IEEE 3rd IAPR Asian conference on pattern recognition (ACPR), 730-734

  34. Liu C, Gardner SJ, Wen N, Elshaikh MA, Siddiqui F, Movsas B, Chetty IJ (2019) Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys 104(4):924–932

    Article  Google Scholar 

  35. Manikis GC, Sakkalis V, Zabulis X, Karamaounas P, Triantafyllou A, Douma S, Zamboulis C, Marias K (2011) An image analysis framework for the early assessment of hypertensive retinopathy signs. E-Health and Bioengineering Conference (EHB) 2011:1–6

  36. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després JP, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jiménez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler ER III, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB (2016) Executive summary: heart disease and stroke Statistics-2016 update: a report from the American Heart Association. Circulation 133(4):447–454

    Article  Google Scholar 

  37. Muramatsu C, Hatanaka Y, Iwase T, Hara T, Fujita H (2011) Automated selection of major arteries and veins for measurement of arteriolar-to-venular diameter ratio on retinal fundus images. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 35(6):472–480

    Article  Google Scholar 

  38. Narasimhan K, Neha VC, Vijayarekha K (2012) Hypertensive retinopathy diagnosis from fundus images by estimation of AVR. Procedia Engineering 38:980–993

    Article  Google Scholar 

  39. Nath, M, and Dandapat, S (2012). Detection of changes in color fundus images due to diabetic retinopathy. 2012 2nd National Conference on computational intelligence and signal processing (CISP), 81-85

  40. Niu, D, Xu, P, Wan, C, Cheng, J, and Liu, J (2017). Automatic localization of optic disc based on deep learning in fundus images. In 2017 IEEE 2nd international conference on signal and image processing (ICSIP) (pp. 208-212)

  41. Noronha, K, NavyaK, T, and Nayak, KP (2013). Support system for the automated detection of hypertensive retinopathy using fundus images. IJCA special issue on international conference on electronic design and signal processing ICEDSP, 1,7–11

  42. Ortiz, D, Cubides, M, Suarez, A, Zequera, ML, Quiroga, J, Gómez, JL, and Arroyo, N (2010). Support system for the preventive diagnosis of hypertensive retinopathy. 2010 annual international conference of the IEEE engineering in medicine and biology, 5649–5652

  43. Pires R, Jelinek HF, Wainer J, Valle E, Rocha A (2014) Advancing bag-of-visual-words representations for lesion classification in retinal images. PloS one 9(6):e96814

    Article  Google Scholar 

  44. Prentasic, P, and Loncaric, S (2015). Detection of exudates in fundus photographs using convolutional neural networks. 2015 9th international symposium on image and signal processing and analysis (ISPA), 188-192

  45. 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

    Article  MathSciNet  Google Scholar 

  46. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  MathSciNet  MATH  Google Scholar 

  47. Rosendorff C, Lackland DT, Allison M, Aronow WS, Black HR, Blumenthal RS, Gersh BJ (2015) Treatment of hypertension in patients with coronary artery disease: a scientific statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension. J Am Coll Cardiol 65(18):1998–2038

    Article  Google Scholar 

  48. Saez M, González-Vázquez S, Penedo MG, Barceló MA, Pena-Seijo M, Tuero GC, Pose-Reino A (2012) Development of an automated system to classify retinal vessels into arteries and veins. Comput Methods Prog Biomed 108(1):367–376

    Article  Google Scholar 

  49. Sahoo, AK, Pradhan, C, and Das, H (2020). Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. Nature inspired computing for data science, 201-212

  50. Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V (2020) Ophthalmic diagnosis using deep learning with fundus images - a critical review. Artif Intell Med 102:101758

    Article  Google Scholar 

  51. Simonyan, K, and Zisserman, A (2014). Very deep convolutional networks for large-scale image recognition. Computing research repository (CoRR), abs/1409.1556

  52. Soomro TA, Afifi AJ, Zheng L, Soomro S, Gao J, Hellwich O, Paul M (2019) Deep learning models for retinal blood vessels segmentation: a review. IEEE Access 7:71696–71717

    Article  Google Scholar 

  53. Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Ginneken BV (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509

    Article  Google Scholar 

  54. Syahputra, M.F., Amalia, C., Rahmat, R.F., Abdullah, D., Napitupulu, D., Setiawan, M.I., Albra, W., Nurdin, and Andayani, U. (2018). Hypertensive retinopathy identification through retinal fundus image using backpropagation neural network. Journal of Physics: Conference Series, 978

  55. Szegedy, C, Vanhoucke, V, Ioffe, S, Shlens, J, and Wojna, Z (2016). Rethinking the inception architecture for computer vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818–2826

  56. Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70–79

    Article  Google Scholar 

  57. Tramontan L, Ruggeri A (2009) Computer estimation of the AVR parameter in diabetic retinopathy. In: Dössel O, Schlegel WC (eds) World Congress on Medical Physics and Biomedical Engineering, September 7–12, 2009, Munich, Germany. IFMBE Proceedings, 25 11. Springer, Berlin, Heidelberg

    Google Scholar 

  58. Triwijoyo BK, Budiharto W, Abdurachman E (2017) The classification of hypertensive retinopathy using convolutional neural network. Procedia Computer Science 116:166–173

    Article  Google Scholar 

  59. Triwijoyo, BK, and Pradipto, YD (2017). Detection of hypertension retinopathy using deep learning and Boltzmann machines. Journal of physics: conference series, 801 1, 012039

  60. Vaghefi E, Yang S, Hill S, Humphrey G, Walker N, Squirrell D (2019) Detection of smoking status from retinal images; a convolutional neural network study. Sci Rep 9(1):1–9

    Article  Google Scholar 

  61. Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Computational intelligence and neuroscience 2018:1–13

    Google Scholar 

  62. Wang L, Liu G, Fu S, Xu L, Zhao K, Zhang C (2016) Retinal image enhancement using robust inverse diffusion equation and self-similarity filtering. PLoS One 11:7

    Google Scholar 

  63. Welikala RA, Foster PJ, Whincup P, Rudnicka AR, Owen CG, Strachan DP, Barman S (2017) Automated arteriole and venule classification using deep learning for retinal images from the UK biobank cohort. Comput Biol Med 90:23–32

    Article  Google Scholar 

  64. Wiharto W and Suryani E (2019). The review of computer aided diagnostic hypertensive retinopathy based on the retinal image processing. IOP Conf. Series: Materials Science and Engineering, 620

  65. Wu S, Zhong S, Liu Y (2018) Deep residual learning for image steganalysis. Multimed Tools Appl 77(9):10437–10453

    Article  Google Scholar 

  66. Yao, Z, Zhang, Z, and Xu, L (2016). Convolutional neural network for retinal blood vessel segmentation. The 9th international symposium on computational intelligence and design (ISCID), 1, 406-409

  67. Yosinski, J, Clune, J, Bengio, Y, and Lipson, H (2014). How transferable are features in deep neural networks?. Adv Neural Inf Proces Syst, 3320–3328

  68. Zhao M, Kang M, Tang B, Pecht M (2017) Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Trans Ind Electron 65(5):4290–4300

    Article  Google Scholar 

  69. Zhu C, Zou B, Zhao R, Cui J, Duan X, Chen Z, Liang Y (2017) Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput Med Imaging Graph 55:68–77

    Article  Google Scholar 

  70. Zou, X, Zhao, X, Yang, Y, and Li, N (2016). Learning-based visual saliency model for detecting diabetic macular edema in retinal image Computational intelligence and neuroscience, 2016

Download references

Acknowledgements

This study was funded by Al Imam Muhammad Ibn Saud Islamic University (IMSIU) (grant number 360905).

Author information

Authors and Affiliations

  1. Department of Computer Science, Al Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia

    Qaisar Abbas & Mostafa E. A. Ibrahim

  2. Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Qalubia, Benha, Egypt

    Mostafa E. A. Ibrahim

Authors
  1. Qaisar Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mostafa E. A. Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qaisar Abbas.

Ethics declarations

Conflict of interest

Author declares that there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbas, Q., Ibrahim, M.E.A. DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning. Multimed Tools Appl 79, 31595–31623 (2020). https://doi.org/10.1007/s11042-020-09630-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09630-x

Keywords

Search

Navigation