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
[1] ALISON N J,DJAMAL B.Ultrasound image segmentation:a survey.IEEE Transactions on Medical Imaging,2006,25(8):987-1010.
[2] YU-LEN H,DAR-REN C.Watershed segmentation for breast tumorin 2-d sonography.Ultrasound in Medicine & Biology,2004,30(5):625-632.
[3] GAETANO R,MASI G,POGGI G,et al.Markercontrolledwatershed-based segmentation of multiresolution remote sensingimages.IEEE Transactions on Geoscience & Remote Sensing,2015,53(6):2987-3004.
[4] NAIMI H,ADAMOU-MITICHE A B H,MITICHE L.Medical imagedenoising using Dual Tree Complex Thresholding Wavelet Transformand Wiener filter.Journal of King Saud University-Computer and Information Sciences,2015,27(1):40-45.
[5] PHAM V N,LONG T N,NGUYEN T D.Feature-reductionfuzzyco-clustering algorithm for hyperspectral image segmentation//IEEE International Conference on Fuzzy Systems.2017.
[6] ZHAO Y,RADA L,CHEN K,et al.Automatedvessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images.IEEE Transactions on Medical Imaging,2015,34(9):1797-1807.
[7] FAN X,JU L,WANG X,et al.A fuzzy edge-weighted centroidalvoronoi tessellation model for image segmentation.Computers &Mathematics with Applications,2016,71(11):2272-2284.
[8] WANG H,HUANG T Z,XU Z,et al.An active contour modeland its algorithms with local and global gaussian distribution fitting energies.Information Sciences An International Journal,2014,263(1):43-59.
[9] KONUR U,GRGEN F S,VAROL F,et al.Computer aided detection of spina bifida using nearest neighbor classification withcurvature scale space features of fetal skulls extracted from ultrasound images.Knowledge-Based Systems,2015,85(C):80-95.
[10] FAROKHI S,SHEIKH U U,FLUSSER J,et al.Near infrared face recognition using zernike moments and hermite kernels.Information Sciences An International Journal,2015,316(C):234-245.
[11] WENG T,YUAN Y,LING S,et al.Clothing image retrievalu-sing color moment//International Conference on Computer Science & Network Technology.2014.
[12] YAMAGUCHI J,YONEYAMA A,MINAMOTO T.Automatic detection of early esophageal cancer from endoscope image using fractal dimension and discrete wavelet transform//International Conference on Information Technology-new Generations.2015.
[13] DAS J,ROY H.Human face detection in color images using hsvcolor histogram and wld//International Conference on Computational Intelligence & Communication Networks.2015.
[14] XU X,QUAN C,REN F.Facial expression recognition based ongabor wavelet transform and histogram of oriented gradients//IEEE International Conference on Mechatronics & Automation.2015.
[15] EMMANUEL A,OLUGBARA O O.Lung cancer prediction usingneural network ensemble with histogram of oriented gradient genomicfeatures.The Scientific World Journal,2015,2015:786013.
[16] MOCANU D C,AMMAR H B,LOWET D,et al.Factored four way conditional restricted Boltzmann machines for activity recognition.Pattern Recognition Letters,2015,66(C):100-108.
[17] KOREZ R,LIKAR B,PERNU F,et al.Model-based segmentationof vertebral bodies from mr images with 3d cnns//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer,Cham,2016:433-441.
[18] MOESKOPS P,WOLTERINK J M,VELDEN B H M V D,et al.Deep learningfor multi-task medical image segmentation in multiple modalities//International Conference on Medical Image Computing & Computerassisted Intervention.2016.
[19] GULSHAN V,PENG L,CORAM M,et al.Development andvalidation of a deep learning algorithm fordetection of diabetic retinopathy in retinal fundus photographs.Jama,2016,316(22):2402.
[20] ESTEVA A,KUPREL B,NOVOA R A,et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature,2017,542(7639):115-118.
[21] MANSANET J,ALBIOL A,PAREDES R,et al.Mask selec-tive regularization for restricted boltzmann machines.Neurocomputing,2015,165(C):375-383.
[22] ANTONY J,MCGUINNESS K,CONNOR N E O,et al.Quantifying radio graphic knee osteoarthritis severity using deep convolutional neural networks.2016.
[23] BENGTSSON E,MALM P.Screening for cervical cancer using automated analysis of pap-smears.Computational and Mathematical Methodsin Medicine,2014,2014(2962):842037.
[24] KOOI T,LITJENS G,GINNEKEN B V,et al.Large scale deep learning for computer aided detection of mammographic lesions.Medical Image Analysis,2017,35:303-312.
[1] YU Xue-yong, CHEN Tao. Privacy Protection Offloading Algorithm Based on Virtual Mapping in Edge Computing Scene [J]. Computer Science, 2021, 48(1): 65-71.
[2] SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing. L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing [J]. Computer Science, 2021, 48(1): 89-95.
[3] HE Yan-hui, WU Gui-xing, WU Zhi-qiang. Domain Alignment Based Object Detection of X-ray Images [J]. Computer Science, 2021, 48(1): 175-181.
[4] LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min. Survey on Target Site Prediction of Human miRNA Based on Deep Learning [J]. Computer Science, 2021, 48(1): 209-216.
[5] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[6] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[7] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[8] ZHANG Yan-mei, LOU Yin-cheng. Deep Neural Network Based Ponzi Scheme Contract Detection Method [J]. Computer Science, 2021, 48(1): 273-279.
[9] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[10] ZHUANG Shi-jie, YU Zhi-yong, GUO Wen-zhong, HUANG Fang-wan. Short Term Load Forecasting via Zoneout-based Multi-time Scale Recurrent Neural Network [J]. Computer Science, 2020, 47(9): 105-109.
[11] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[12] ZHANG Jia-jia, ZHANG Xiao-hong. Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability [J]. Computer Science, 2020, 47(9): 129-134.
[13] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[14] ZHU Ling-ying, SANG Qing-bing, GU Ting-ting. No-reference Stereo Image Quality Assessment Based on Disparity Information [J]. Computer Science, 2020, 47(9): 150-156.
[15] ZHAO Qin-yan, LI Zong-min, LIU Yu-jie, LI Hua. Cascaded Siamese Network Visual Tracking Based on Information Entropy [J]. Computer Science, 2020, 47(9): 157-162.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .