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An Ultrasonic Image Recognition Method for Papillary Thyroid Carcinoma Based on Depth Convolution Neural Network

  • Wei Ke
  • Yonghua WangEmail author
  • Pin Wan
  • Weiwei Liu
  • Hailiang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

The ultrasonic image of thyroid papillary carcinoma is characterized by two dimensional gray scale, low resolution, and complicated internal tissue structure. The characteristics of thyroid papillary carcinoma are not obvious and it is difficult to be distinguished. In this paper, the convolution neural network (CNN) theory is introduced for the automatic identification of the ultrasonic image of thyroid papillary carcinoma. Based on the improvement of the Faster RCNN, a detection method for the identification of ultrasonic image features of papillary thyroid carcinoma is proposed, that is, by connecting the fourth and fifth layers of the shared convolution layer in the Faster RCNN, and then normalizing. Secondly, multi-scale ultrasonic images are used in the input. Finally, thyroid papillary carcinoma is classified according to several major characteristics of its ultrasound image.so that the detailed ultrasound image diagnostic report can be received. The experimental results show that the recognition accuracy of the Faster RCNN is higher, the training time is shorter and the efficiency is higher compared with that of the original Faster RCNN.

Keywords

Convolution neural network Thyroid papillary carcinoma Ultrasound images 

Notes

Acknowledgment

This work was supported in part by the Degree and Graduate Education Reform Project of Guangdong Province under Grant No. 2016JGXM_MS_26, Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education under Grant No. MSC-201706A and Higher Education Quality Project of Guangdong Province.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wei Ke
    • 1
  • Yonghua Wang
    • 1
    • 2
    Email author
  • Pin Wan
    • 1
  • Weiwei Liu
    • 3
  • Hailiang Li
    • 4
  1. 1.School of AutomationGuangdong University of TechnologyGuangzhouChina
  2. 2.Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of EducationSun Yat-Sen UniversityGuangzhouChina
  3. 3.Sun Yat-Sen University Cancer CenterGuangzhouChina
  4. 4.School of Electronics and Information TechnologySun Yat-Sen UniversityGuangzhouChina

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