Abstract
Macular disease is one of the major causes of blindness. Optical coherence tomography (OCT) is a commonly used ophthalmic diagnostic technique to assist ophthalmologists in their analysis and treatment. However, manual analysis is a time-consuming, heavy and subjective process. In this paper, we propose an improved EfficientNet model for retinal OCT image classification, named TL-RA-EfficientNet. It introduces a Re-Attention module to enable the model to identify the lesions with small areas and fuzzy shapes. During training, a data augmentation strategy is introduced to solve the class-imbalance problem, and a transfer learning strategy is adopted to speed up the convergence of the model. The experimental results on the UCSD dataset show that the average recognition accuracy, sensitivity and specificity of our model are 99.9%, 99.9% and 99.97%, respectively, which are more effective than previous classification methods.
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
Kokame, G.T., Omizo, J.N., Kokame, K.A., Yamane, M.L.: Differentiating exudative macular degeneration and polypoidal choroidal vasculopathy using OCT B-scan. Ophthalmol. Retina 5(10), 954–961 (2021)
Goh, J.K.H., Cheung, C.Y., Sim, S.S., Tan, P.C., Tan, G.S.W., Wong, T.Y.: Retinal imaging techniques for diabetic retinopathy screening. J. Diabetes Sci. Technol. 10(2), 282–294 (2016)
Adhi, M., Duker, J.S.: Optical coherence tomography – current and future applications. Curr. Opin. Ophthalmol. 24(3), 213–221 (2013)
Wang, Y., et al.: A DNN for arrhythmia prediction based on ECG. In: Huang, Z., Siuly, S., Wang, H., Zhou, R., Zhang, Y. (eds.) HIS 2020. LNCS, vol. 12435, pp. 146–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61951-0_14
Qi, Y., Lin, S., Huang, Z.: Classification of skin pigmented lesions based on deep residual network. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds.) HIS 2019. LNCS, vol. 11837, pp. 58–67. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32962-4_6
Puneet, Kumar, R., Gupta, M.: Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Inf. Sci. Syst. 10, 13 (2022)
Zhang, X., et al.: Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images. Health Inf. Sci. Syst. 10(1), 1–12 (2022). https://doi.org/10.1007/s13755-022-00170-2
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), Kuching, Malaysia, pp. 489–492. IEEE (2017)
Bhowmik, A., Kumar, S., Bhat, N.: Eye disease prediction from optical coherence tomography images with transfer learning. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds.) EANN 2019. CCIS, vol. 1000, pp. 104–114. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20257-6_9
Fang, L., Wang, C., Li, S., Rabbani, H., Chen, X., Liu, Z.: Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification. IEEE Trans. Med. Imaging 38(8), 1959–1970 (2019)
Mishra, S.S., Mandal, B., Puhan, N.: Perturbed composite attention model for macular optical coherence tomography image classification. IEEE Trans. AI 3, 625–635 (2021)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: 36th International Conference on Machine Learning, Los Angeles, pp. 6105–6114 (2019)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Tezduyar, T.E., Mittal, S., Ray, S.E., Shih, R.: Incompressible flow computations with stabilized bilinear and linear equal-order-interpolation velocity-pressure elements. Comput. Methods Appl. Mech. Eng. 95(2), 221–242 (1992)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Islam, K.T., Wijewickrema, S., O’Leary, S.: Identifying diabetic retinopathy from OCT images using deep transfer learning with artificial neural networks. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 281–286 (2019)
Chen, S., Chen, M., Ma, W.: Research on automatic classification of optical coherence tomography retina image based on multi-channel. Chin. J. Lasers 48(23), 109–118 (2021)
Roy, A.G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel “Squeeze and Excitation.” IEEE Trans. Med. Imaging 38(2), 540–549 (2019)
Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., Barnard, K.: Attentional feature fusion. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3560–3569 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lian, X., Chen, L., Ji, X., Shen, F., Guo, H., Gao, H. (2022). Optical Coherence Tomography Classification Based on Transfer Learning and RA-Attention. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-20627-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20626-9
Online ISBN: 978-3-031-20627-6
eBook Packages: Computer ScienceComputer Science (R0)