Journal of Innovative Optical Health Sciences, Volume. 16, Issue 6, 2350008(2023)

Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach

Lantian Hu... Ruixiang Guo, Sifan Li, Jing Cao* and Qian Liu |Show fewer author(s)
Author Affiliations
  • Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, P. R. China
  • show less
    References(38)

    [1] D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito. Optical coherence tomography. Science, 254, 1178-1181(1991).

    [2] T. C. Chen, A. Hoguet, A. K. Junk, K. Nouri-Mahdavi, S. Radhakrishnan, H. L. Takusagawa, P. P. Chen. Spectral-domain OCT: Helping the clinician diagnose glaucoma: A report by the American Academy of Ophthalmology. Ophthalmology, 125, 1817-1827(2018).

    [3] A. Pujari, K. Bhaskaran, P. Sharma, P. Singh, S. Phuljhele, R. Saxena, S. V. Azad. Optical coherence tomography angiography in neuro-ophthalmology: Current clinical role and future perspectives. Surv. Ophthalmol., 66, 471-481(2021).

    [4] H. Li, K. Liu, L. Yao, X. Deng, Z. Zhang, P. Li. ID-OCTA: OCT angiography based on inverse SNR and decorrelation features. J. Innov. Opt. Health Sci., 14, 2130001(2021).

    [5] U. Schmidt-Erfurth, A. Sadeghipour, B. S. Gerendas, S. M. Waldstein, H. Bogunović. Artificial intelligence in retina. Prog. Retin. Eye Res., 67, 1-29(2018).

    [6] R. Kapoor, B. T. Whigham, L. A. Al-Aswad. Artificial intelligence and optical coherence tomography imaging. Asia-Pacific J. Ophthalmol., 8, 187-194(2019).

    [7] D. S. W. Ting, L. R. Pasquale, L. Peng, J. P. Campbell, A. Y. Lee, R. Raman, G. S. W. Tan, L. Schmetterer, P. A. Keane, T. Y. Wong. Artificial intelligence and deep learning in ophthalmology. Br. J. Ophthalmol., 103, 167-175(2019).

    [8] P. Ongsulee. Artificial intelligence, machine learning and deep learning. 2017 15th Int. Conf. ICT and Knowledge Engineering (ICT&KE), 1-6(2017).

    [9] J. Liu, S. Yan, N. Lu, D. Yang, C. Fan, H. Lv, S. Wang, X. Zhu, Y. Zhao, Y. Wang. Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images. J. Innov. Opt. Health Sci., 15, 2242001(2022).

    [10] G. Zheng, Y. Jiang, C. Shi, H. Miao, X. Yu, Y. Wang, S. Chen, Z. Lin, W. Wang, F. Lu. Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images. J. Innov. Opt. Health Sci., 14, 2140002(2021).

    [11] P. Prahs, V. Radeck, C. Mayer, Y. Cvetkov, N. Cvetkova, H. Helbig, D. Märker. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefe’s Arch. Clin. Exp. Ophthalmol., 256, 91-98(2018).

    [12] D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172, 1122-1131(2018).

    [13] V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316, 2402-2410(2016).

    [14] Z. Jiang, L. Wang, Q. Wu, Y. Shao, M. Shen, W. Jiang, C. Dai. Computer-aided diagnosis of retinopathy based on vision transformer. J. Innov. Opt. Health Sci., 15, 2250009(2022).

    [15] J. Kim, L. Tran. Retinal disease classification from OCT images using deep learning algorithms. 2021 IEEE Conf. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1-6(2021).

    [16] Z. Chen, Z. Zeng, H. Shen, X. Zheng, P. Dai, P. Ouyang. DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images. Biomed. Signal Process. Control, 55, 101632(2020).

    [17] M. Koziarski, B. Cyganek. Image recognition with deep neural networks in presence of noise–dealing with and taking advantage of distortions. Integr. Comput.-Aided Eng., 24, 337-349(2017).

    [18] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., 16, 2080-2095(2007).

    [19] B. Qiu, Z. Huang, X. Liu, X. Meng, Y. You, G. Liu, K. Yang, A. Maier, Q. Ren, Y. Lu. Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function. Biomed. Opt. Exp., 11, 817-830(2020).

    [20] P. Gholami, P. Roy, M. K. Parthasarathy, V. Lakshminarayanan. OCTID: Optical coherence tomography image database. Comput. Electr. Eng., 81, 106532(2020).

    [21] M. Li, R. Idoughi, B. Choudhury, W. Heidrich. Statistical model for OCT image denoising. Biomed. Opt. Exp., 8, 3903-3917(2017).

    [22] B. Baumann, C. W. Merkle, R. A. Leitgeb, M. Augustin, A. Wartak, M. Pircher, C. K. Hitzenberger. Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?. Biomed. Opt. Exp., 10, 5755-5775(2019).

    [23] A. Zhang, J. Xi, J. Sun, X. Li. Pixel-based speckle adjustment for noise reduction in Fourier-domain OCT images. Biomed. Opt. Exp., 8, 1721-1730(2017).

    [24] J. Wang, G. Deng, W. Li, Y. Chen, F. Gao, H. Liu, Y. He, G. Shi. Deep learning for quality assessment of retinal OCT images. Biomed. Opt. Exp., 10, 6057–6072(2019).

    [25] X. Liu, M. Tanaka, M. Okutomi. Single-image noise level estimation for blind denoising. IEEE Trans. Image Process., 22, 5226-5237(2013).

    [26] K. Weiss, T. M. Khoshgoftaar, D. Wang. A survey of transfer learning. J. Big Data, 3, 1-40(2016).

    [27] L. Torrey, J. Shavlik. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques(2010).

    [28] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis., 115, 211-252(2015).

    [29] D. Maji, A. Santara, P. Mitra, D. Sheet. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images(2016).

    [30] G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger. Densely connected convolutional networks. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 4700-4708(2017).

    [31] C. Szegedy, S. Ioffe, V. Vanhoucke, A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI Conf. Artificial Intelligence, 4278-4284(2017).

    [32] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 770-778(2016).

    [33] J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks. Pro. IEEE Conf. Computer Vision and Pattern Recognition, 7132-7141(2018).

    [34] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition(2014).

    [35] N. Chinchor. MUC-4 evaluation metrics. Proc. Fourth Message Understanding Conf., 22-29(1992).

    [36] D. D. Lewis, R. E. Schapire, J. P. Callan, R. Papka. Training algorithms for linear text classifiers. Proc. 19th Annual Int. ACM SIGIR Conf. Research and Development in Information Retrieval, 298-306(1996).

    [37] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proc. IEEE Int. Conf. Computer Vision, 618-626(2017).

    [38] C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, C.-W. Lin. Deep learning on image denoising: An overview. Neural Netw., 131, 251-275(2020).

    Tools

    Get Citation

    Copy Citation Text

    Lantian Hu, Ruixiang Guo, Sifan Li, Jing Cao, Qian Liu. Accuracy improvement for classifying retinal OCT images by diseases using deep learning-based selective denoising approach[J]. Journal of Innovative Optical Health Sciences, 2023, 16(6): 2350008

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Dec. 24, 2022

    Accepted: Mar. 21, 2023

    Published Online: Dec. 23, 2023

    The Author Email: Cao Jing (caoj@hainanu.edu.cn)

    DOI:10.1142/S1793545823500086

    Topics