Objective Design and develop a intelligent cytopathological lung cancer diagnosing system(ICLCDS) utilizing the latest computer technologies(including Reinforcement Lcaming Multiple Classifier Fusion and Dimcnsionality Reduction) and the cy-topathological knowledge on lung canccrcclls Methods We got information ofcclls and segregated cell regions in a slice image using an magi scgmcntouon a址orithm Sascd on reinforcement lcaming including rcconstmction of overlapped cell area Sascd on B一Spline and improved dcBoor-Cox Mcthoc} We comSincd multiple classifiers including Baycsian classific:Support Vector Machine(SVM) classific K-Ncarcst NcighSour( KNN) and Decision c classific to achieve an accurate result of cytopathological lung cancer diag-nosis Results Experimental results on 1 200 cases randomly selected we as follows the accurate diagnosis rate for lung cancer idcn-tification was the false positive rate was 1. 8`J /c‘the false negative rate was 3. 3`J /c‘the type class

计算机辅助肺癌细胞病理诊断的初步研究.caj

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