中国数字医学
中國數字醫學
중국수자의학
CHINA DIGITAL MEDICINE
2013年
6期
78-81
,共4页
Adaboost%SVM-SCAD%不平衡数据
Adaboost%SVM-SCAD%不平衡數據
Adaboost%SVM-SCAD%불평형수거
Adaboost%SVM-SCAD%imbalanced data
CT血管造影在肺栓塞临床诊断中有着广泛的应用，然而血管造影大都包含上百张肺部切片，人工筛选这些切片效率低下出错率较高。尝试通过组合学习算法建立自动识别模型来有效提高识别能力，识别模型由三部分构成：数据的不平衡处理，变量选择方法和组合学习模型。通过比较不同不平衡数据处理策略和变量选择方法的基础上，选择Adaboost方法进行分类算法的学习，临床数据的结果表明该方法能较好地辅助实际诊断。
CT血管造影在肺栓塞臨床診斷中有著廣汎的應用，然而血管造影大都包含上百張肺部切片，人工篩選這些切片效率低下齣錯率較高。嘗試通過組閤學習算法建立自動識彆模型來有效提高識彆能力，識彆模型由三部分構成：數據的不平衡處理，變量選擇方法和組閤學習模型。通過比較不同不平衡數據處理策略和變量選擇方法的基礎上，選擇Adaboost方法進行分類算法的學習，臨床數據的結果錶明該方法能較好地輔助實際診斷。
CT혈관조영재폐전새림상진단중유착엄범적응용，연이혈관조영대도포함상백장폐부절편，인공사선저사절편효솔저하출착솔교고。상시통과조합학습산법건립자동식별모형래유효제고식별능력，식별모형유삼부분구성：수거적불평형처리，변량선택방법화조합학습모형。통과비교불동불평형수거처리책략화변량선택방법적기출상，선택Adaboost방법진행분류산법적학습，림상수거적결과표명해방법능교호지보조실제진단。
Computed Tomography Angiography (CTA) has become an accurate diagnostic tool for Pulmonary Emboli (PE). However, each CTA study consists of hundreds of images, each representing one slice of the lung. Manual reading of these slices is an inefficiency job with high misclassification rate. This paper is aimed to build models which can automatically identify the disease through a combination of learning algorithms. The proposed diagnostic model consists of three parts: the imbalance data processing, variable selection procedure and Boosting algorithm model. After comparing the result of different unbalance data processing strategies and different variable selection methods, the Bootstrap Adaboost learning model is chosen to conduct our classification task. Finally, results in clinical data shows that our model performed well.
