Research on Recognizing Gastric Cancer Cell Based on Back Propagation Neural Network_Chinese Journal of Evidence-Based Medicine

Authors：

 CHEN Xianlai ¹² , XIAO Xiaodan ¹ , YANG Rong ³ , LIU Jianping ⁴

1. Xiangya School of Medicine, Central South University, Changsha 410013, China2. College of Information Science and Engineering, Central South University, Changsha 410083, China3. Department of Gastroenterological Surgery, Xiangya Hospital, Central South University, Changsha 410078, China4. College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China;

Corresponding author：

CHEN Xianlai, Email: Chenxianlai@xysm.net

Keywords：

Back propagation neural network; Gastric cancer; Cell recognizing

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Objective　 To investigate the value of back propagation (BP) neural network for recognizing gastric cancer cell.
Methods 　A total of 510 cells was selected from 308 patients. There were 210 gastric adenocarcinoma cells and 300 non-cancer gastric cells. Ten morphological parameters were measured for each cell. These data were randomly divided into two groups: training dataset (A) and test dataset (B). A three-layer BP neural network was built and trained by using dataset A. The network was then tested with dataset A and B.
Results　 For data A, the sensitivity of network was 99%, specificity 99%, positive predictive value 98%, negative predictive value 99%, and accuracy 99%. For data B, the sensitivity of network was 99%, specificity 97%, positive predictive value 96%, negative predictive value 99%, the accuracy 98%. With receiver operator characteristic (ROC) curve evaluation, the area under ROC curve was 0.99.
Conclusion 　The model based on BP neural network is very effective. A BP neural network can be used for effectively recognizing gastric cancer cell.

Citation： CHEN Xianlai,XIAO Xiaodan,YANG Rong,LIU Jianping. Research on Recognizing Gastric Cancer Cell Based on Back Propagation Neural Network. Chinese Journal of Evidence-Based Medicine, 2007, 07(9): 637-640. doi: Copy

1.	杨育彬, 李宁, 陈世福, 陈兆乾. 肺癌分类识别中的神经网络集成技术研究. 计算机科学, 2003, 30(9): 39-42, 53.
2.	Celenk M, Yinglei Song ML, et al. Shape classification of malignant lymphomas and leukemia by morphological watersheds and ARMA modeling. Proceedings of the SPIE. The International Society for Optical Engineering, 2003, 5032: 265-276.
3.	Chen WJ, Meer P, Georgescu B, et al. Image mining for investigative pathology using optimized feature extraction and data fusion. Computer Methods and Programs in Biomedicine, 2005, 79(1): 59-72.
4.	Fawcett T. ROC Graphs : Notes and Practical Considerations for Researchers[R/OL]. Tech. Report HPL-2003-4, 2003. [2005] http://www.purl.org/NET/tfawcett/papers/ROC101.pdf.
5.	Weiss GM. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 7-19.
6.	Chawla NV, Japkowicz N, Kolcz A(editors). ICML’2003 Workshop on Learning from Imbalanced Data Sets[C/OL[2003 ]. http://www.site.uottawa.ca/~nat/Workshop2003/workshop2003.html.
7.	Japkowica N (editor). Proc of the AAAI’2000 Workshop on Learning form Imbalanced Data Sets. AAAI Tech Report WS-00-05, AAAI, 2000.
8.	Bradley AP. Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition, 1997, 30(7): 1145-1159.
9.	Chawla NV, Japkowicz N, Kolcz A. Editorial : Special Issue on Learning from Imbalanced Data Sets[C]//ACM SIGKDD Exploration, 2004, 6(1) : 1 - 6.
10.	Theodoridis S, Koutroumbas K. Pattern Recognition. 2nd ed. Elsevier Science, 2003: 173-174.

1. 杨育彬, 李宁, 陈世福, 陈兆乾. 肺癌分类识别中的神经网络集成技术研究. 计算机科学, 2003, 30(9): 39-42, 53.
2. Celenk M, Yinglei Song ML, et al. Shape classification of malignant lymphomas and leukemia by morphological watersheds and ARMA modeling. Proceedings of the SPIE. The International Society for Optical Engineering, 2003, 5032: 265-276.
3. Chen WJ, Meer P, Georgescu B, et al. Image mining for investigative pathology using optimized feature extraction and data fusion. Computer Methods and Programs in Biomedicine, 2005, 79(1): 59-72.
4. Fawcett T. ROC Graphs : Notes and Practical Considerations for Researchers[R/OL]. Tech. Report HPL-2003-4, 2003. [2005] http://www.purl.org/NET/tfawcett/papers/ROC101.pdf.
5. Weiss GM. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 7-19.
6. Chawla NV, Japkowicz N, Kolcz A(editors). ICML’2003 Workshop on Learning from Imbalanced Data Sets[C/OL[2003 ]. http://www.site.uottawa.ca/~nat/Workshop2003/workshop2003.html.
7. Japkowica N (editor). Proc of the AAAI’2000 Workshop on Learning form Imbalanced Data Sets. AAAI Tech Report WS-00-05, AAAI, 2000.
8. Bradley AP. Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition, 1997, 30(7): 1145-1159.
9. Chawla NV, Japkowicz N, Kolcz A. Editorial : Special Issue on Learning from Imbalanced Data Sets[C]//ACM SIGKDD Exploration, 2004, 6(1) : 1 - 6.
10. Theodoridis S, Koutroumbas K. Pattern Recognition. 2nd ed. Elsevier Science, 2003: 173-174.

Chinese Journal of Evidence-Based Medicine

Research on Recognizing Gastric Cancer Cell Based on Back Propagation Neural Network

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