Laser & Optoelectronics Progress, Volume. 56, Issue 24, 241003(2019)

Malignant Thyroid Nodule Detection Based on Circular Convolutional Neural Network

Bin Zheng1, Chen Yang1, Xiaoping Ma2, and Libo Liu1、*
Author Affiliations
  • 1School of Information Engineering, Ningxia University, Yinchuan, Ningxia 750021, China
  • 2Medical Technologic Departments, Yinchuan People's Hospital, Yinchuan, Ningxia 750002, China
  • show less
    Figures & Tables(10)
    Experimental procedure of circular CNN
    Ultrasound images of thyroid. (a) Original image; (b) noise-removal image; (c) region of interest
    Basic principle of circular CNN
    Structure of CNN-Fusion
    Activation function plots. (a) Sigmoid; (b) ReLUs
    Experimental data. (a) Accuracy rate; (b) Loss value
    Loss curves of ReLUs and Sigmoid functions
    Feature maps of ultrasound image of thyroid
    • Table 1. Image numbers of thyroid classification

      View table

      Table 1. Image numbers of thyroid classification

      DatasourceClassification ⅠClassification ⅡClassification Ⅲ
      NormalNoduleBenignMalignantMalignantHighly deteriorated
      Local21571101609492288288
      Romero213714580580
      Total215711018221206868868
      Test648330246361260260
    • Table 2. Comparison of classification results for good and malignancy

      View table

      Table 2. Comparison of classification results for good and malignancy

      MethodAccuracy /%
      ANN[8]88.24
      SVM[8]76.47
      LOOCV[9]86.60
      TND[10]65.50
      ResNet[19]92.40
      Proposed method94.30
    Tools

    Get Citation

    Copy Citation Text

    Bin Zheng, Chen Yang, Xiaoping Ma, Libo Liu. Malignant Thyroid Nodule Detection Based on Circular Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(24): 241003

    Download Citation

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

    Category: Image Processing

    Received: Apr. 8, 2019

    Accepted: Jun. 6, 2019

    Published Online: Nov. 26, 2019

    The Author Email: Liu Libo (liulib@163.com)

    DOI:10.3788/LOP56.241003

    Topics