Abstract
Retinal disease classification is an important challenge in computer aided diagnosis (CAD) for medical applications. Eye diseases can cause different symptoms from mild vision problems to complete blindness if it is not timely treated. The early diagnosis is crucial to prevent blindness. In this work, we use deep Convolutional Neural Networks (CNN) on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases using Optical Coherence Tomography (OCT) images. The obtained results achieve state-of-the-art performance and show that the proposed network leads to higher classification rates with an accuracy of 98.46%, and an Area Under Curve (AUC) of 0.998. An explainability algorithm was also developed and shows the efficiency of the proposed approach in detecting retinal disease signs.
This work was supported in part by the New Brunswick Health Research Foundation (NBHRF). The NVIDIA Quadro P6000 was donated by the NVIDIA Corporation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Awais, M., Müller, H., Tang, T.B., Meriaudeau, F.: Classification of SD-OCT images using a deep learning approach. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 489–492. IEEE (2017)
Chan, G.C., Kamble, R., Müller, H., Shah, S.A., Tang, T., Mériaudeau, F.: Fusing results of several deep learning architectures for automatic classification of normal and diabetic macular edema in optical coherence tomography. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 670–673. IEEE (2018)
Chan, G.C., Muhammad, A., Shah, S.A., Tang, T.B., Lu, C.K., Meriaudeau, F.: Transfer learning for diabetic macular edema DME detection on optical coherence tomography OCT images. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 493–496. IEEE (2017)
Chetoui, M., Akhloufi, M.A., Kardouchi, M.: Diabetic retinopathy detection using machine learning and texture features. In: 31st IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2018) (2018)
Chollet, F., et al.: Keras (2015). https://keras.io
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Huang, Y., et al.: GPipe: efficient training of giant neural networks using pipeline parallelism. In: Advances in Neural Information Processing Systems, pp. 103–112 (2019)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. arXiv preprint arXiv:1602.07360 1(10) (2016)
Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography OCT and chest x-ray images for classification. Mendeley data (2018). https://data.mendeley.com/datasets/rscbjbr9sj/2
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Li, F., et al.: Deep learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed. Opt. Express 10(12), 6204–6226 (2019)
Li, F., Chen, H., Liu, Z., Zhang, X., Wu, Z.: Fully automated detection of retinal disorders by image-based deep learning. Graefe’s Arch. Clin. Exp. Ophthalmol. 257(3), 495–505 (2019)
Lu, W., Tong, Y., Yu, Y., Xing, Y., Chen, C., Shen, Y.: Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images. Transl. Vis. Sci. Technol. 7(6), 41–41 (2018)
Malik, S., Kanwal, N., Asghar, M.N., Sadiq, M.A.A., Karamat, I., Fleury, M.: Data driven approach for eye disease classification with machine learning. Appl. Sci. 9(14), 2789 (2019)
NVIDIA: QUADRO P6000. https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-pascal-p6000-data-sheet-us-nv-704590-r1.pdf. Accessed Feb 2020
Perdomo, O., Otálora, S., González, F.A., Meriaudeau, F., Müller, H.: 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), pp. 1423–1426. IEEE (2018)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arxiv 2014. arXiv preprint arXiv:1409.1556 1409 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. CoRR abs/1905.11946 (2019). http://arxiv.org/abs/1905.11946
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chetoui, M., Akhloufi, M.A. (2020). Deep Retinal Diseases Detection and Explainability Using OCT Images. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-50516-5_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50515-8
Online ISBN: 978-3-030-50516-5
eBook Packages: Computer ScienceComputer Science (R0)