• Laser & Optoelectronics Progress
  • Vol. 58, Issue 1, 117002 (2021)
Lian Chaoming1, Zhong Shuncong1、*, Zhang Tianfu1, Zhou Ning1, and Xie Maosong2
Author Affiliations
  • 1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China
  • 2The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China
  • show less
    DOI: 10.3788/LOP202158.0117002 Cite this Article Set citation alerts
    Lian Chaoming, Zhong Shuncong, Zhang Tianfu, Zhou Ning, Xie Maosong. Transfer Learning-Based Classification of Optical Coherence Tomography Retinal Images[J]. Laser & Optoelectronics Progress, 2021, 58(1): 117002 Copy Citation Text show less
    References

    [1] Chen D W, Ran X W. New drugs for the treatment of diabetes mellitus[J]. Chinese Journal of Diabetes Mellitus, 10, 103-106(2018).

    [2] Guidelines for clinical diagnosis and treatment of diabetic retinopathy in Ophthalmology(2014)[J]. Chinese Journal of Ophthalmology, 50, 851-865(2014).

    [3] Huang D, Swanson E A, Lin C P et al. Optical coherence tomography[J]. Science, 254, 1178-1181(1991).

    [4] Isaac D L C, Avila M. Diabetic retinopathy and OCT angiography: clinical findings and future perspectives[J]. International Journal of Retina and Vitreous, 3, 1-10(2017).

    [5] Gao Y Z, Yuan Y, Ma Z H. High-resolution cortical blood flow imaging based on optical coherence tomography[J]. Laser & Optoelectronics Progress, 56, 111101(2019).

    [6] Xu L X, Chen X J, Ban Y et al. Method for intelligent detection of parking spaces based on deep learning[J]. Chinese Journal of Lasers, 46, 0404013(2019).

    [7] Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 124, 962-969(2017).

    [8] Ding P L, Li Q Y, Zhang Z et al. Diabetic retinal image classification method based on deep neural network[J]. Journal of Computer Applications, 37, 699-704(2017).

    [9] Pang H, Wang C. Deep learning model for diabetic retinopathy detection[J]. Journal of Software, 28, 3018-3029(2017).

    [10] Zhuang F Z, Luo P, He Q et al. Survey on transfer learning research[J]. Journal of Software, 26, 26-39(2015).

    [11] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

    [12] Venkateswara H, Chakraborty S, Panchanathan S. Deep-learning systems for domain adaptation in computer vision: learning transferable feature representations[J]. IEEE Signal Processing Magazine, 34, 117-129(2017).

    [13] Hsiao T Y, Chang Y C, Chou H H et al. Filter-based deep-compression with global average pooling for convolutional networks[J]. Journal of Systems Architecture, 95, 9-18(2019).

    [14] Lin D Y, Sun L, Toh K A et al. Twin SVM with a reject option through ROC curve[J]. Journal of the Franklin Institute, 355, 1710-1732(2018). http://www.sciencedirect.com/science/article/pii/S0016003217302260

    [15] Zhang C X, Chen M H, Wang F et al. Optical coherence tomography image denoising algorithm based on wavelet transform and fractional integral[J]. Laser & Optoelectronics Progress, 56, 181008(2019).

    [16] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [17] Bhowmik A, Kumar S, Bhat N. Eye disease prediction from optical coherence tomography images with transfer learning. [C]//Engineering Applications of Neural Networks. [S.l.:s.n.], 104-114(2019).

    [18] Wang C, He X X, Fang L Y et al. Automatic classification of retinal optical coherence tomography images via convolutional neural networks with joint decision[J]. Chinese Journal of Biomedical Engineering, 37, 641-648(2018).

    Lian Chaoming, Zhong Shuncong, Zhang Tianfu, Zhou Ning, Xie Maosong. Transfer Learning-Based Classification of Optical Coherence Tomography Retinal Images[J]. Laser & Optoelectronics Progress, 2021, 58(1): 117002
    Download Citation