基于不完全贝叶分类模型的淋巴系造影图像对淋巴肿瘤诊断的算法研究  

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

作　　者：纪琳[1] 宋彬[1] 吴双[2] 

机构地区：[1]四川大学华西医院放射科,成都610041 [2]浙江大学计算机科学与技术学院,杭州310027

出　　处：《生物医学工程学杂志》2014年第3期499-501,共3页Journal of Biomedical Engineering

基　　金：中央高校基本科研业务费专项基金资助项目(ZYGX2011J070)

摘　　要：随着疾病谱的改变,恶性肿瘤已经成为危害人们身体健康的常见疾病,了解有无淋巴结转移,对早期诊断肿瘤性疾病,合理治疗有重要的指导意义。淋巴造影是影像学判断淋巴结病例状况的"金标准",它是唯一能观察淋巴管道内部结构的方法,从而鉴别良性反应性淋巴结肿大和淋巴结肿瘤。本文使用不完全朴素贝叶斯分类模型构建计算机辅助诊断模型,利用淋巴系造影(Lymphography)数据集对实验模型进行检验,选择最优的分类算法构建模型,辅助医师的教学、诊断。仿真实验验证了多种不完全朴素贝叶斯模型对淋巴结能有效地判别。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 Classifica- tion. 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.

关 键 词：不完全朴素贝叶斯 淋巴系造影 计算机辅助诊断 

分 类 号：R733.1[医药卫生—肿瘤] TP391.41[医药卫生—临床医学]