Laser & Optoelectronics Progress, Volume. 55, Issue 8, 81001(2018)

Breast Cancer Diagnosis System Based on Transfer Learning and Deep Convolutional Neural Networks

Chu Jinghui, Wu Zerui, Lü Wei, and Li Zhe
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    References(22)

    [1] [1] Pei C D, Wang C M, Xu S Z. Segmentation of the breast region in mammograms using marker-controlled watershed transform[C]. The 2nd IEEE International Conference on Information Science and Engineering, 2010: 2371-2374.

    [2] [2] Ries L A G, Harkins D, Krapcho M, et al. SEER cancer statistics review, 1975-2003[R]. Bethesda: National Cancer Institute, 2006.

    [3] [3] Fenton J J, Taplin S H, Carney P A, et al. Influence of computer-aided detection on performance of screening mammography[J]. New England Journal of Medicine, 2007, 356(14): 1399-1409.

    [4] [4] Tang J, Rangayyan R M, Xu J, et al. Computer-aided detection and diagnosis of breast cancer with mammography: recent advances[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(2): 236-251.

    [5] [5] Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42: 60-88.

    [6] [6] Yoon S, Kim S. Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms[J]. Pattern Recognition Letters, 2009, 30(16): 1489-1495.

    [7] [7] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359.

    [8] [8] Japkowicz N. Learning from imbalanced data sets: a comparison of various strategies[C]. AAAI Workshop on Learning from Imbalanced Data Sets, 2000, 68: 10-15.

    [9] [9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012: 1097-1105.

    [10] [10] Sharif R A, Azizpour H, Sullivan J, et al. CNN features off-the-shelf: an astounding baseline for recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014: 806-813.

    [11] [11] Boureau Y L, Bach F, LeCun Y, et al. Learning mid-level features for recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2010: 2559-2566.

    [12] [12] Le Q V. Building high-level features using large scale unsupervised learning[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, 2013: 8595-8598.

    [13] [13] Bar Y, Diamant I, Wolf L, et al. Chest pathology detection using deep learning with non-medical training[J]. IEEE 12th International Symposium on Biomedical Imaging, 2015: 294-297.

    [14] [14] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]. International Conference on Learning Representations, 2015.

    [15] [15] Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.

    [16] [16] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

    [17] [17] Khan S A, Yong S P. An evaluation of convolutional neural nets for medical image anatomy classification[J]. Advances in Machine Learning and Signal Processing, 2016: 293-303.

    [18] [18] Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.

    [20] [20] Kitanovski I, Jankulovski B, Dimitrovski I, et al. Comparison of feature extraction algorithms for mammography images[C]. The 4th International Congress on Image and Signal Processing, 2011: 888-892.

    [21] [21] Hong J. Gray level-gradient cooccurrence matrix texture analysis method[J]. Acta Automatica Sinica, 1984, 10(1): 22-25.

    [22] [22] Galloway M M. Texture analysis using gray level run lengths[J]. Computer Graphics and Image Processing, 1975, 4(2): 172-179.

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    Chu Jinghui, Wu Zerui, Lü Wei, Li Zhe. Breast Cancer Diagnosis System Based on Transfer Learning and Deep Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2018, 55(8): 81001

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    Paper Information

    Category: Image Processing

    Received: Oct. 13, 2017

    Accepted: --

    Published Online: Aug. 13, 2018

    The Author Email:

    DOI:10.3788/lop55.081001

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