Skip to main content
Springer Nature Link
Log in

Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection prevents severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent systems are proven to be beneficial for the assessment of glaucoma. In this paper, we describe an approach to automate the diagnosis of glaucoma disease, based on color funds photography using deep learning. The setup of the proposed framework is ordered as follows: The bidimensional empirical mode decomposition (BEMD) algorithm is applied to decompose the ROI to components (BIMFs + residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. The features obtained are the input parameters of the implemented classifier based on full connect layers and softmax. To train the built model, we have used the public dataset RIM-ONE DL. To test our models, we have used a part of RIM-ONE DL and REFUGE. The average obtained sensitivity, specificity, accuracy and AUC rates are, respectively, 99.14%, 99.19%, 99.13%, 99.09% and 99.17%, 99.24%, 99.20%, 99.18% in RIM-ONE DL and REFUGE dataset. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://www.who.int/.

Abbreviations

AI:

Artificial intelligence

BEMD:

Bidimensional empirical mode decomposition

BIMF:

Bidimensional intrinsic mode functions

CAD:

Computer-aided diagnosis

CNN:

Convolutional neural network

DL:

Deep learning

FC:

Fully connected layer

OD:

Optic disk

REFUGE:

Retinal fundus glaucoma challenge

RIM-ONE:

Retinal iMage database for optic nerve evaluation

ROI:

Regions of interest

VGG:

Visual geometry group

References

  1. Weinreb, R.N., Aung, T., Medeiros, F.A.: The pathophysiology and treatment of glaucoma: a review. JAMA 311(18), 1901–1911 (2014)

    Article  Google Scholar 

  2. Tham, Y.-C., Li, X., Wong, T.Y., Quigley, H.A., Aung, T., Cheng, C.-Y.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081–2090 (2014)

    Article  Google Scholar 

  3. Quigley, H.A., Broman, A.T.: the number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90(3), 262–267 (2006)

    Article  Google Scholar 

  4. Baudouin, C., Kolko, M., Melik-Parsadaniantz, S., Messmer, E.M.: Inflammation in glaucoma: from the back to the front of the eye, and beyond. Prog. Retin. Eye Res. 83, 100916 (2021)

    Article  Google Scholar 

  5. Pesapane, F., Codari, M., Sardanelli, F.: Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur. Radiol. Exp. 2(1), 1–10 (2018)

    Article  Google Scholar 

  6. Lakhani, P., Prater, A.B., Hutson, R.K., Andriole, K.P., Dreyer, K.J., Morey, J., Prevedello, L.M., Clark, T.J., Geis, J.R., Itri, J.N., et al.: Machine learning in radiology: applications beyond image interpretation. J. Am. Coll. Radiol. 15(2), 350–359 (2018)

    Article  Google Scholar 

  7. Elmoufidi, A., Skouta, A., Jai-Andaloussi, S., Ouchetto, O.: CNN with multiple inputs for automatic glaucoma assessment using fundus images. Int. J. Image Graph. 2350012 (2022)

  8. Kotsiantis, S.B., Zaharakis, I., Pintelas, P., et al.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160(1), 3–24 (2007)

    Google Scholar 

  9. Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)

    Google Scholar 

  10. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)

    Article  Google Scholar 

  11. Sujan, M., Scott, P., Cresswell, K. Digital health and patient safety: technology is not a magic wand (2020)

  12. Thanh, D.N.H., Hai, N.H., Tiwari, P., Prasath, V.B.S., et al.: Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation. Comput. Opt. 45(1) (2021)

  13. Zafar, K., Gilani, S.O., Waris, A., Ahmed, A., Jamil, M., Khan, M.N., Sohail Kashif, A.: Skin lesion segmentation from dermoscopic images using convolutional neural network. Sensors 20(6), 1601 (2020)

    Article  Google Scholar 

  14. Yadav, N., Alfayeed, S.M., Khamparia, A., Pandey, B., Thanh, D.NH., Pande, S.: HSV model-based segmentation driven facial acne detection using deep learning. Expert Syst e12760 (2021)

  15. Khamparia, A., Bharati, S., Podder, P., Gupta, D., Khanna, A., Phung, T.K., Thanh, D.N.H.: diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimension. Syst. Signal Process. 32(2), 747–765 (2021)

    Article  MATH  Google Scholar 

  16. Kumar, V., Mishra, B.K., Mazzara, M., Thanh, D.N.H., Verma, A.: Prediction of malignant and benign breast cancer: a data mining approach in healthcare applications. In: Advances in Data Science and Management, pp. 435–442. Springer, Berlin (2020)

  17. Elmoufidi, A., El Fahssi, K., Jai-Andaloussi, S., Sekkaki, A., Gwenole, Q., Lamard, M.: Anomaly classification in digital mammography based on multiple-instance learning. IET Image Proc. 12(3), 320–328 (2018)

    Article  Google Scholar 

  18. Elmoufidi, A., El Fahssi, K., Jai-Andaloussi, S., Madrane, N., Sekkaki, A.: Detection of regions of interest’s in mammograms by using local binary pattern, dynamic k-means algorithm and gray level co-occurrence matrix. In: 2014 International Conference on Next Generation Networks and Services (NGNS), pp. 118–123. IEEE (2014)

  19. Elmoufidi, A.: Pre-processing algorithms on digital x-ray mammograms. In: 2019 IEEE International Smart Cities Conference (ISC2), pp. 87–92. IEEE (2019)

  20. Than, D.N.H, Sergey, D., Prasath, V.B.S., Hai, N.H.: Blood vessels segmentation method for retinal fundus images based on adaptive principal curvature and image derivative operators. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences (2019)

  21. Skouta, A., Elmoufidi, A., Jai-Andaloussi, S., Ochetto, O.: Automated binary classification of diabetic retinopathy by convolutional neural networks. In: Advances on Smart and Soft Computing, pp. 177–187. Springer, Berlin (2021)

  22. Chakravarty, A., Sivaswamy, J.: Glaucoma classification with a fusion of segmentation and image-based features. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 689–692. IEEE (2016)

  23. Maheshwari, S., Pachori, R.B., Acharya, U.R.: Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J. Biomed. Health Inform. 21(3), 803–813 (2016)

    Article  Google Scholar 

  24. Acharya, U.R., Dua, S., Du, X., Chua, C.K., et al.: Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans. Inf Technol. Biomed. 15(3), 449–455 (2011)

    Article  Google Scholar 

  25. Dua, S., Acharya, U.R., Chowriappa, P., Sree, S.V.: Wavelet-based energy features for glaucomatous image classification. IEEE Trans. Inf Technol. Biomed. 16(1), 80–87 (2011)

    Article  Google Scholar 

  26. Diaz-Pinto, A., Morales, S., Naranjo, V., Köhler, T., Mossi, J.M., Navea, A.: CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed. Eng. Online 18(1), 1–19 (2019)

    Article  Google Scholar 

  27. Orlando, J.I., Huazhu, F., Breda, J.B., van Keer, K., Bathula, D.R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J.H., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)

    Article  Google Scholar 

  28. Bajwa, M.N., Malik, M.I., Siddiqui, S.A., Dengel, A., Shafait, F., Neumeier, W., Ahmed, S.: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med. Inform. Decis. Mak. 19(1), 1–16 (2019)

    Google Scholar 

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

    Article  Google Scholar 

  30. Orlando, J.I., Prokofyeva, E., del Fresno, M., Blaschko, M.B.: Convolutional neural network transfer for automated glaucoma identification. In: 12th International Symposium on Medical Information Processing and Analysis, vol. 10160, p. 101600U. International Society for Optics and Photonics (2017)

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

    Article  Google Scholar 

  32. Elmoufidi, A.: Deep multiple instance learning for automatic breast cancer assessment using digital mammography. IEEE Trans. Instrum. Meas. (2022)

  33. El-Dahshan, E.-S.A., Mohsen, H.M., Revett, K., Salem, A.-B.M.: Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Zhaohua, W., Huang, N.E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 460(2046), 1597–1611 (2004)

    Article  MATH  Google Scholar 

  36. Huang, W., Shen, Z., Huang, N.E., Fung, Y.C.: Use of intrinsic modes in biology: examples of indicial response of pulmonary blood pressure to \(\pm \) step hypoxia. Proc. Natl. Acad. Sci. 95(22), 12766–12771 (1998)

    Article  Google Scholar 

  37. Song, H., Bai, Y., Pinheiro, L., Dong, C., Huang, X., Liu, B.: Analysis of ocean internal waves imaged by multichannel reflection seismics, using ensemble empirical mode decomposition. J. Geophys. Eng. 9(3), 302–311 (2012)

    Article  Google Scholar 

  38. Garcia-Perez, A., Amezquita-Sanchez, J.P., Dominguez-Gonzalez, A., Sedaghati, R., Osornio-Rios, R., Romero-Troncoso, R.J.: Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. J. Zhejiang Univ. Sci. A 14(9), 615–630 (2013)

    Article  Google Scholar 

  39. Zheng, J., Cheng, J., Yang, Yu.: Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis. Mech. Syst. Signal Process. 40(1), 136–153 (2013)

    Article  Google Scholar 

  40. Zhu, K., Song, X., Xue, D.: Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient. J. Vibroengineering 15(2), 597–603 (2013)

    Google Scholar 

  41. Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P.: Image analysis by bidimensional empirical mode decomposition. Image Vis. Comput. 21(12), 1019–1026 (2003)

    Article  MATH  Google Scholar 

  42. Nunes, J.C., Guyot, S., Deléchelle, E.: Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach. Vis. Appl. 16(3), 177–188 (2005)

    Article  Google Scholar 

  43. Zhou, Y., Li, H.: Adaptive noise reduction method for DSPI fringes based on bi-dimensional ensemble empirical mode decomposition. Opt. Express 19(19), 18207–18215 (2011)

    Article  Google Scholar 

  44. Linderhed, A.: 2d empirical mode decompositions in the spirit of image compression. In: Wavelet and Independent Component Analysis Applications IX, vol. 4738, pp. 1–8. International Society for Optics and Photonics (2002)

  45. Linderhed, A.: Compression by image empirical mode decomposition. In: IEEE International Conference on Image Processing 2005, vol. 1, pp. I–553. IEEE (2005)

  46. Qiao, L., Niu, K.F., Wang, N., Peng, L.: Perfect reconstruction image modulation based on BEMD and quaternionic analytic signals. Sci. China Inf. Sci. 54(12), 2602–2614 (2011)

    Article  MathSciNet  Google Scholar 

  47. Chen, Y., Wang, L., Sun, Z., Jiang, Y., Zhai, G.: Fusion of color microscopic images based on bidimensional empirical mode decomposition. Opt. Express 18(21), 21757–21769 (2010)

    Article  Google Scholar 

  48. Linderhed, A.: Adaptive image compression with wavelet packets and empirical mode decomposition. Citeseer (2004)

  49. Yang, B.-S., Fengshou, G., Ball, A., et al.: Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis. Mech. Syst. Signal Process. 38(2), 601–614 (2013)

    Article  Google Scholar 

  50. Liu, Z., Peng, S.: Boundary processing of bidimensional EMD using texture synthesis. IEEE Signal Process. Lett. 12(1), 33–36 (2004)

    Google Scholar 

  51. He, Z., Wang, Q., Shen, Y., Jin, J., Wang, Y.: Multivariate gray model-based BEMD for hyperspectral image classification. IEEE Trans. Instrum. Meas. 62(5), 889–904 (2013)

    Article  Google Scholar 

  52. Huang, N.E., Shen, Z., Long, S.R., ManliC, W., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  53. Guo, F., Mai, Y., Zhao, X., Duan, X., Fan, Z., Zou, B., Xie, B.: Yanbao: a mobile app using the measurement of clinical parameters for glaucoma screening. IEEE Access 6, 77414–77428 (2018)

    Article  Google Scholar 

  54. Batista, F.J.F., Diaz-Aleman, T., Sigut, J., Alayon, S., Arnay, R., Angel-Pereira, D.: RIM-ONE DL: a unified retinal image database for assessing glaucoma using deep learning. Image Anal. Stereol. 39(3), 161–167 (2020)

    Article  MATH  Google Scholar 

  55. Fumero, F., Alayón, S., Sanchez, J.L., Sigut, J., Gonzalez-Hernandez, M.: RIM-ONE: an open retinal image database for optic nerve evaluation. In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. IEEE (2011)

Download references

Funding

This study has no funding.

Author information

Authors and Affiliations

  1. Data4Earth Laboratory, Sultan Moulay Slimane University, 523, Beni Mellal, Morocco

    Abdelali Elmoufidi

  2. Computer and Systems Laboratory, Hassan II University, Casablanca, 20100, Morocco

    Ayoub Skouta, Said Jai-andaloussi & Ouail Ouchetto

Authors
  1. Abdelali Elmoufidi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ayoub Skouta
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Said Jai-andaloussi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ouail Ouchetto
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Abdelali Elmoufidi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elmoufidi, A., Skouta, A., Jai-andaloussi, S. et al. Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography. Prog Artif Intell 11, 397–409 (2022). https://doi.org/10.1007/s13748-022-00292-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-022-00292-4

Keywords