Abstract
Diabetic retinopathy occurs due to damage to the blood vessels in the retina, and it is a major health problem in recent years that progresses slowly without recognizable symptoms. Optical coherence tomography (OCT) is a popular and widely used noninvasive imaging modality for the diagnosis of diabetic retinopathy. Accurate and early diagnosis of this disease using OCT images is crucial for the prevention of blindness. In recent years, several deep learning methods have been very successful in automating the process of detecting retinal diseases from OCT images. However, most methods face reliability and interpretability issues. In this study, we propose a deep residual network for the classification of four classes of retinal diseases, namely diabetic macular edema (DME), choroidal neovascularization (CNV), DRUSEN and NORMAL in OCT images. The proposed model is based on the popular architecture called ResNet50, which eliminates the vanishing gradient problem and is pre-trained on large dataset such as ImageNet and trained end-to-end on the publicly available OCT image dataset. We removed the fully connected layer of ResNet50 and placed our new fully connected block on top to improve the classification accuracy and avoid overfitting in the proposed model. The proposed model was trained and evaluated using different performance metrics, including receiver operating characteristic (ROC) curve on a dataset of 84,452 OCT images with expert disease grading as DRUSEN, CNV, DME and NORMAL. The proposed model provides an improved overall classification accuracy of 99.48% with only 5 misclassifications out of 968 test samples and outperforms existing methods on the same dataset. The results show that the proposed model is well suited for the diagnosis of retinal diseases in ophthalmology clinics.
Graphical abstract
Similar content being viewed by others
References
https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment Accessed January 15, 2021
Bagci AM, Ansari R, Shahidi M (2007) A method for detection of retinal layers by optical coherence tomography image segmentation. IEEE/NIH Life Sci Syst Appl Workshop. https://doi.org/10.1109/LSSA.2007.4400905
Fercher AF (1996) Optical coherence tomography. J Biomed Opt 1(2):157–173. https://doi.org/10.1117/12.231361
Regar E, Schaar JA, Mont E, Virmani R, Serruys PW (2003) Optical coherence tomography. Cardiovasc Radiat Med 4(4):198–204. https://doi.org/10.1016/j.carrad.2003.12.003
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. https://doi.org/10.2147/OPTH.S27656
Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Wang J, Deng G, Li W, Chen Y, Gao F, Liu H, He Y, Shi G (2019) Deep learning for quality assessment of retinal OCT images. Biomed Opt Express 10(12):6057–6072. https://doi.org/10.1364/BOE.10.006057
Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F (2017) Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 16(1):1–2. https://doi.org/10.1186/s12938-017-0352-9
Awais M, Müller H, Tang TB, Meriaudeau F (2017) Classification of sd-oct images using a deep learning approach. IEEE International Conference on Signal and Image Processing Applications (ICSIPA). https://doi.org/10.1109/ICSIPA.2017.8120661
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. https://doi.org/10.1001/jama.2016.17216
Sunija AP, Kar S, Gayathri S, Gopi VP, Palanisamy P (2021) Octnet: A lightweight CNN for retinal disease classification from optical coherence tomography images. Comput Methods Programs Biomed 200:105877. https://doi.org/10.1016/j.cmpb.2020.105877
Rong Y, Xiang D, Zhu W, Yu K, Shi F, Fan Z, Chen X (2018) Surrogate-assisted retinal OCT image classification based on convolutional neural networks. IEEE J Biomed Health Inform 23(1):253–263. https://doi.org/10.1109/JBHI.2018.2795545
Tayal A, Gupta J, Solanki A, Bisht K, Nayyar A, Masud M (2021) DL-CNN-based approach with image processing techniques for the diagnosis of retinal diseases. Multimedia Syst. https://doi.org/10.1007/s00530-021-00769-7
Rajagopalan N, Narasimhan V, Kunnavakkam Vinjimoor S, Aiyer J (2021) Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. J Ambient Intell Humaniz Comput 12(7):7569–7580. https://doi.org/10.1007/s12652-020-02460-7
Hussain MA, Bhuiyan A, Luu DC, Theodore Smith R, Guymer R, Ishikawa H, Schuman SJ, Ramamohanarao K (2018) Classification of the healthy and diseased retina using SD-OCT imaging and Random Forest algorithm. PloS One 13(6):e0198281. https://doi.org/10.1371/journal.pone.0198281
Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GM, Izatt JA, Farsiu S (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. https://doi.org/10.1364/BOE.5.003568
Li F, Chen H, Liu Z, Zhang XD, Jiang MS, Wu ZZ, Zhou KQ (2019) Deep learning based automated detection of retinal diseases using optical coherence tomography images. Biomed Opt Express 10(12):6204–6226. https://doi.org/10.1364/BOE.10.006204
Lam C, Yi D, Guo M, Lindsey T (2018) Automated detection of diabetic retinopathy using deep learning. AMIA Summits on translational science proceedings pp 147–155.
Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131. https://doi.org/10.1016/j.cell.2018.02.010
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Confer Computer Vision Pattern Recogn. https://doi.org/10.1109/CVPR.2016.90
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. https://doi.org/10.1016/j.jvcir.2019.01.022
Hwang DK, Hsu CC, Chang KJ, Chao D, Sun CH, Jheng YC, Yarmishyn AA, Wu JC, Tsai CY, Wang ML, Peng CH (2019) Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics 9(1):232. https://doi.org/10.7150/thno.28447
Alqudah AM (2020) AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Compu 58(1):41–53. https://doi.org/10.1007/s11517-019-02066-y
Saleh N, Abdel Wahed M, Salaheldin AM (2021) Transfer learning-based platform for detecting multi-classification retinal disorders using optical coherence tomography images. Int J Imaging Syst Technol. https://doi.org/10.1002/ima.22673
Funding
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
For this type of study, formal consent is not required.
Human and animal rights
This paper does not contain any studies with human participants or animals performed by any of the authors.
Rights and permissions
About this article
Cite this article
Asif, S., Amjad, K. & Qurrat-ul-Ain Deep Residual Network for Diagnosis of Retinal Diseases Using Optical Coherence Tomography Images. Interdiscip Sci Comput Life Sci 14, 906–916 (2022). https://doi.org/10.1007/s12539-022-00533-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12539-022-00533-z