Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 260-267.doi: 10.11896/JsJkx.191200011

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Automatic Tumor Recognition in Ultrasound Images Based on Multi-model Optimization

GU Wan-rong1, FAN Wei-Jiang1, XIE Xian-fen2, ZHANG Zi-ye3, MAO Yi-Jun1, LIANG Zao-qing1 and LIN Zhen-xi1   

  1. 1 School of Mathematics and Information,South China Agricultural University,Guangzhou 510642,China
    2 School of Economy,Jinan University,Guangzhou 510632,China
    3 School of Mathematical,South China University of Technology,Guangzhou 510641,China
  • Published:2020-07-07
  • About author:GU Wan-rong, born in 1982, Ph.D, assistant professor.His main research interests include machine learning, information retrieval and recommendation.
    MAO Yi-Jun, born in 1979, Ph.D, assistant professor.His main research inte-rests include machine learning, bioinformatics and algorithm.
  • Supported by:
    This work was supported by the Guangdong Natural Science Foundation ProJect (2018A030313437),13th Five-year Plan ProJect of Philosophy and Social Science in Guangdong Province (GD18CXW01),Guangdong Science and Technology Program ProJect (2018A070712021),Ministry of Education Humanities and Social Sciences Research Youth Fund ProJect (18YJCZH037) and 2019 National Statistical Science Research Key ProJect(2019LZ37).

Abstract: With the development of computer vision recognition technology,more and more researchers apply this technology to the recognition of tumor images.But because of the cost,many hospitals still use low-cost ultrasound and other equipment,resulting in ambiguity,artifacts and many similar tumor noise areas.The present method has high precision in clear image recognition,but it shows low accuracy and unstable result in ultrasonic image.The reason is that many existing algorithms misJudge the mo-dulus and noise image.In this paper,the key features of high-noise ultrasound images are obtained quickly and accurately by R-CNN and PRN methods,and the stability of recognition is ensured by data enhancement and morphological filtering.At the same time,the classification model of blood flow signal is fused to improve the recognition accuracy.Based on the data set of a real Thyroid neoplasm image,the proposed method is more accurate and stable than the new algorithm.

Key words: Deep learning, Fusion model, Tumor recognition, Neural network

CLC Number: 

  • TN957.52
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