The Research of Lymph Node Tumor Diagnosis Algorithm for Lymphography Based on Semi-Naive Bayes Classification Model_Journal of Biomedical Engineering

Authors：

 JILin ¹ , SONGBin ¹ , WUShuang ²

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China;
2. College of Computer Science, Zhejiang University, Hangzhou 310027, China;

Corresponding author：

JILin, Email: jilin2@sina.com

Keywords：

Semi-Naive Bayes; lymphography; computer aided diagnose

DOI：

10.7507/1001-5515.20140092

Video：

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Acquaintance of the lymph node is very important to evaluate whether the tumor is malignant or benign and therefore to the treatment of the tumors. Lymphography is now considered the gold standard for this purpose. Many computer aided diagnose (CAD) technologies have been developed to help radiologists to diagnose the tumor by the lymphography cases. In this paper, a computer aided diagnose model is constructed by Semi-naive Bayes Classification. The experiments carried out in our laboratory validated the Semi-Naive Bayes Classification on lymphography case. The result of experiments showed that Semi-Naive Bayes Classification could classify lymphography case effectively.

Citation： JILin, SONGBin, WUShuang. The Research of Lymph Node Tumor Diagnosis Algorithm for Lymphography Based on Semi-Naive Bayes Classification Model. Journal of Biomedical Engineering, 2014, 31(3): 499-501. doi: 10.7507/1001-5515.20140092 Copy

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1. RUNGE V M,DICKEY K M,WILLIAMS N M, et al. Local tissue toxicity in response to extravascular extravasation of magnetic resonance contrast media[J]. Invest Radiol, 2002, 37(7):393-398.
2. 纪琳, 惠孛, 吴双. 基于贝叶斯分类模型的乳腺X线摄影辅助诊断的研究[J].生物医学工程学杂志, 2011,28(3):475-478.
3. HUI B, YANG Y, WEBB G I. Anytime classification for a pool of instances[J]. Mach Learn, 2009, 77(1):61-102.
4. ZHENG F, WEBB G I, SURAWEERA P, et al.Subsumption resolution:an efficient and effective technique for Semi-Naive Bayesian learning[J].Mach Learn, 2012, 87(1):93-125.
5. ZHENG F, WEBB G I. A comparative study of Semi-Naive Bayes methods in classification learning[C]//4th Australasian Data Mining Conference (AusDM05), Sydney:2005:141-156.
6. SAHMAI M. Learning limited dependence Bayesian classifiers[C]//2nd Int Conf Knowledge Discovery in Database, Menlo Park:1996:334-338.
7. WEBB G I, BOUGHTON J R, WANG Z H. Not so naive Bayes:averaged onedependence estimators[J]. Mach Learn, 2005, 58(1):5-24.
8. 惠孛,吴跃.基于不完全朴素贝叶斯分类模型的垃圾邮件分类模型的研究[J].计算机应用, 2009, 29(3):903-904,907.
9. Wikipedia. Mutual information[EB/OL].[2013.2.26]. http://en.wikipedia.org/wiki/Mutual_information.
10. WITTEN I H, EIBE F, HALL M A. Data mining:practical machine learning tools and techniques[M].3rd ed. Burlington:Morgan Kaufmann Publishers, 2011:1-584.
11. Igor Kononenko, Bojan Cestnik. Lymphography Data Set[DB/OL].(1988-11-01).[2013.2.26]. http://archive.ics.uci.edu/ml/datasets/Lymphography.

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