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AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images

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Abstract

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.

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References

  1. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131 e9

    Article  CAS  Google Scholar 

  2. Farsiu S, Chiu SJ, O'Connell RV, Folgar FA, Yuan E, Izatt JA, Toth CA, Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study Group (2014) Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1):162–172

    Article  Google Scholar 

  3. Kanagasingam Y, Bhuiyan A, Abramoff MD, Smith RT, Goldschmidt L, Wong TY (2014) Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res 38:20–42

    Article  Google Scholar 

  4. Lemaitre G et al (2016) Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection. J Ophthalmol 2016:3298606

    PubMed  PubMed Central  Google Scholar 

  5. Reis AS et al (2012) Influence of clinically invisible, but optical coherence tomography detected, optic disc margin anatomy on neuroretinal rim evaluation. Invest Ophthalmol Vis Sci 53(4):1852–1860

    Article  Google Scholar 

  6. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunovic H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29

    Article  Google Scholar 

  7. Esteva A et al (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    Article  CAS  Google Scholar 

  8. Ting DSW et al (2019) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103(2):167–175

    Article  Google Scholar 

  9. Ting DSW, Wu WC, Toth C (2018) Deep learning for retinopathy of prematurity screening. Br J Ophthalmol

  10. Sajda P (2006) Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng 8:537–565

    Article  CAS  Google Scholar 

  11. Pierro L, Zampedri E, Milani P, Gagliardi M, Isola V, Pece A (2012) Spectral domain OCT versus time domain OCT in the evaluation of macular features related to wet age-related macular degeneration. Clin Ophthalmol 6:219–223

    PubMed  PubMed Central  Google Scholar 

  12. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  13. Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibe D, Meriaudeau F (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 16(1):68

    Article  Google Scholar 

  14. Awais M, Müller H, Tang TB, Meriaudeau F (2017) Classification of sd-oct images using a deep learning approach. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, pp 489–492

  15. Lee CS, Baughman DM, Lee AY (2017) Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. Ophthalmol Retina 1(4):322–327

    Article  Google Scholar 

  16. Karri SP, 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(2):579–592

    Article  CAS  Google Scholar 

  17. Rong Y, Xiang D, Zhu W, Yu K, Shi F, Fan Z, Chen X (2019) Surrogate-assisted retinal OCT image classification based on convolutional neural networks. IEEE J Biomed Health Inform 23(1):253–263

    Article  Google Scholar 

  18. Fang L, Jin Y, Huang L, Guo S, Zhao G, Chen X (2019) Iterative fusion convolutional neural networks for classification of optical coherence tomography images. J Vis Commun Image Represent 59:327–333

    Article  Google Scholar 

  19. Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F (2018) Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging 37(4):1024–1034

    Article  Google Scholar 

  20. Amil P et al (2019) Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. Sci Rep 9(1):1157

    Article  Google Scholar 

  21. Srinivasan PP et al (2014) Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express 5(10):3568–3577

    Article  Google Scholar 

  22. Mehta P, Lee AY, Lee C, Balazinska M, Rokem A (2018) Multilabel multiclass classification of OCT images augmented with age, gender and visual acuity data. bioRxiv:316349

  23. Hussain MA et al (2018) Classification of healthy and diseased retina using SD-OCT imaging and random forest algorithm. PLoS One 13(6):e0198281

    Article  Google Scholar 

  24. Ji Q, He W, Huang J, Sun Y (2018) Efficient deep learning-based automated pathology identification in retinal optical coherence tomography images. Algorithms 11(6)

    Article  Google Scholar 

  25. Perdomo O, Otálora S, González FA, Meriaudeau F, Müller H (2018) Oct-net: a convolutional network for automatic classification of normal and diabetic macular edema using SD-OCT volumes. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp 1423-1426

  26. Hwang DK, Hsu CC, Chang KJ, Chao D, Sun CH, Jheng YC, Yarmishyn AA, Wu JC, Tsai CY, Wang ML, Peng CH, Chien KH, Kao CL, Lin TC, Woung LC, Chen SJ, Chiou SH (2019) Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 9(1):232–245

    Article  Google Scholar 

  27. Nugroho KA (2018) A comparison of handcrafted and deep neural network feature extraction for classifying optical coherence tomography (OCT) images. In: 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), IEEE, pp 1-6

  28. Gnanadurai D, Sadasivam V (2005) Image de-noising using double density wavelet transform based adaptive thresholding technique. International Journal of Wavelets, Multiresolution and Information Processing 03(01):141–152

    Article  Google Scholar 

  29. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980

  30. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  Google Scholar 

  31. Gao F, Yue Z, Wang J, Sun J, Yang E, Zhou H (2017) A novel active semisupervised convolutional neural network algorithm for SAR image recognition. Comput Intell Neurosci 2017:3105053

    PubMed  PubMed Central  Google Scholar 

  32. Bakator M, Radosav D (2018) Deep learning and medical diagnosis: a review of literature. Multimod Techn and Interact 2(3)

    Article  Google Scholar 

  33. Gu J et al (2018) Recent advances in convolutional neural networks. Pattern Recog 77:354–377

    Article  Google Scholar 

  34. Alqudah AM, Alquraan H, Abu-Qasmieh I, Al-Badarneh A (2018) Employing image processing techniques and artificial intelligence for automated eye diagnosis using digital eye fundus images. JBBBE 39:40–56

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to thank the Nvidia Learning Center at Yarmouk University (YU), represented by Dr. Ahmad Alomari, for providing access to the GPU Unit for training and testing the CNN architecture on the whole image dataset and getting the results. Also, the author would like to thank Zain Innovation Campus (ZINC) at Yarmouk University (YU) represented by Eng. Nour Al-Ajlouni, for providing access to the GPU Unit for initial results and testing of the CNN architecture on part of the dataset.

Availability of data and materials

The image dataset that used to test and support the findings of this research is available from different hospitals in Jordan, but restrictions apply to the availability of these data, which were used under license for the current research only, and so are not publicly available. Data are however may be available from the author upon reasonable request and with permission of all hospitals.

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Authors and Affiliations

  1. Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan

    Ali Mohammad Alqudah MSc

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  1. Ali Mohammad Alqudah MSc
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Correspondence to Ali Mohammad Alqudah MSc.

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Alqudah, A.M. AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Comput 58, 41–53 (2020). https://doi.org/10.1007/s11517-019-02066-y

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  • DOI: https://doi.org/10.1007/s11517-019-02066-y

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