LI Jiangyun, ZHANG Jie, CHANG Dedan, HU Yaojun. Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN[J]. Chinese Journal of Electronics, 2019, 28(4): 718-724. doi: 10.1049/cje.2019.03.005
Citation: LI Jiangyun, ZHANG Jie, CHANG Dedan, HU Yaojun. Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN[J]. Chinese Journal of Electronics, 2019, 28(4): 718-724. doi: 10.1049/cje.2019.03.005

Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN

doi: 10.1049/cje.2019.03.005
Funds:  This work is supported by the Fundamental Research Funds for the China Central Universities of USTB (No.FRF-BR-17-004A, No.FRF-GF-17-B49),and the Open Project Program of the National Laboratory of Pattern Recognition (No.201800027).
  • Received Date: 2018-03-12
  • Rev Recd Date: 2018-07-26
  • Publish Date: 2019-07-10
  • The deficiencies of existing polyp detection methods remain:i) They primarily depend on the manually extracted features and require considerable amounts of preprocessing. ii) Most traditional methods cannot specify the location of the polyps in colonoscopy images, especially for the polyps with variable size. In order to derive the improvement and lift the accuracy, we propose a novel and scalable detection algorithm based on deep neural networks-an improved Faster Regionbased Convolutional neural networks (Faster R-CNN)-by increasing the fusion of feature maps at different levels. It can be employed to detect and locate polyps, and even achieve a multi-object task for polyps in the future. The experimental consequences demonstrate that the best version among improved algorithms achieves 97.13% accuracy on the CVC-ClinicDB database, overtaking the previous methods.
  • loading
  • R. Bale and G. Widmann, “Navigated CT-guided interventions”, Minimally Invasive Therapy & Allied Technologies, Vol.16, No.4, pp.196–204, 2007.
    J. Bergstra and Y. Bengio, “Random search for hyperparameter optimization”, Journal of Machine Learning Research, Vol.13, No.Feb, pp.281–305, 2012.
    J. Bernal, J. Sánchez and F. Vilarino, “Towards automatic polyp detection with a polyp appearance model”, Pattern Recognition, Vol.45, No.9, pp.3166–3182, 2012.
    S. Hwang, J.H. Oh, W. Tavanapong, et al., “Polyp detection in colonoscopy video using elliptical shape feature”, International Conference on Image Processing, San Antonio, TX, USA, pp.Ⅱ-465–Ⅱ-468, 2007.
    Y.P. Sun, D. Sargent, et al., “A colon video analysis framework for polyp detection”, IEEE Transactions on BioMedical Engineering, Vol.59, No.5, pp.1408–1418, 2012.
    Y. Wang, W. Tavanapong, J. Wong, et al., “Partbased multiderivative edge cross-sectional profiles for polyp detection in colonoscopy”, IEEE Journal of Biomedical & Health Informatics, Vol.18, No.4, pp.1379–1389, 2014.
    Y. Lecun, Y. Bengio and G. Hinton, “Deep learning”, Nature, Vol.521, No.7553, pp.436–444, 2015.
    R. Zhang, Y. Zheng, W.C. Mak, et al., “Automatic detection and classification of colorectal polyps by transferring low-level CNN features from non-medical domain”, IEEE Journal of Biomedical & Health Informatics, Vol.21, No.1, pp.41–47, 2016.
    Y. Shin and I. Balasingham, “Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification”, Engineering in Medicine and Biology Society. IEEE, Seogwipo, South Korea, pp.3277–3280, 2017.
    Y.F. Zheng, X.W. Zhang, T.Y. Cao, et al., “The semantic salient region detection algorithm based on the fully convolutional networks”, Acta Electronica Sinica, Vol.45, No.11, pp.2593–2601, 2017. (in Chinese)
    W. Bi, W.G. Huang, Y.P. Zhang, et al., “Object detection based on salient contour of image”, Acta Electronica Sinica, Vol.45, No.8, pp.1902–1910, 2017. (in Chinese)
    S. Qiu, D.S. Wen, J. Feng, et al., “A new strategy lung nodules detection algorithm”, Acta Electronica Sinica, Vol.44, No.6, pp.1413–1419, 2016. (in Chinese)
    A. Karpathy, G. Toderici, S. Shetty, et al., “Large-scale video classification with convolutional neural networks”, IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, IEEE Computer Society Washington, DC, USA, pp.1725–1732, 2014.
    R. Girshick, J. Donahue, T. Darrell, et al., “Rich feature hierarchies for accurate object detection and semantic segmentation”, Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Washington, DC, USA, pp.580–587, 2014.
    J.R.R. Uijlings, K.E.A. Van De Sande, T. Gevers, et al. “Selective search for object recognition”, International Journal of Computer Vision, Vol.104, No.2, pp.154–171, 2013.
    S. Ren, K. He, R. Girshick, et al., “Faster R-CNN: Towards real-time object detection with region proposal networks”, Advances in Neural Information Processing Systems, pp.91–99, 2015.
    R. Girshick, “Fast R-CNN”, IEEE International Conference on Computer Vision. IEEE, Santiago, Chile, pp.1440–1448, 2015.
    T.Y. Lin, P. Dollar, R. Girshick, et al., “Feature pyramid networks for object detection”, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp.936–944, 2017.
    D.K. Iakovidis, D.E. Maroulis, S.A. Karkanis, et al. “A comparative study of texture features for the discrimination of gastric polyps in endoscopic video”, 18th IEEE Symposium on Computer-Based Medical Systems, Dublin, Ireland, pp.575–580, 2005.
    S.A. Karkanis, D.K. Iakovidis, D.E. Maroulis, et al., “Computer-aided tumor detection in endoscopic video using color wavelet features”, IEEE Transactions on Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine & Biology Society, Vol.7, No.3, pp.141–152, 2003.
    V. Kodogiannis and H.S. Chowdrey, “Multi network classification scheme for computer-aided diagnosis in clinical endoscopy”, MEDSIP 2004—Advances in Medical Signal and Information Processing Int, Malta. IEE, pp.262–267, 2004.
    M.P. Tjoa and S.M. Krishnan, “Feature extraction for the analysis of colon status from the endoscopic images”, Biomedical Engineering Online, Vol.2, No.1, pp.1–17, 2003.
    M.M. Zheng, S.M. Krishnan and M.P. Tjoa, “A fusion-based clinical decision support for disease diagnosis from endoscopic images”, Computers in Biology & Medicine, Vol.35, No.3, pp.259–274, 2005.
    S. Qiu, D.S. Wen, Y. Cui, et al., “Lung nodules detection in CT images using gestalt-based algorithm”, Chinese Journal of Electronics, Vol.25, No.4, pp.711–718, 2016.
    D.S. Kermany, M. Goldbaum, W. Cai, et al., “Identifying medical diagnoses and treatable diseases by image-based deep learning”, Cell, Vol.172, No.5, pp.1122–1131.e9, 2018.
    R. Nawarathna, J.H. Oh, J. Muthukudage, et al., “Abnormal image detection in endoscopy videos using a filter bank and local binary patterns”, Neurocomputing, Vol.144, No.1, pp.70–91, 2014.
    J. Bernal, F.J. Sánchez, G. Fernández-Esparrach, et al., “WMDOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians”, Comput Med Imaging Graph, Vol.43, No.1, pp.99–111, 2015.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (222) PDF downloads(192) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return