对宫颈细胞进行多分类可以自动识别出不同状态的细胞,进而为宫颈癌诊断提供科学依据.在用6种多分类算法进行实验后,选取支持向量机作为基分类器,先用一对一策略训练6个分类器进行3分类,然后再训练1个2分类器,这种二层4分类方法提高了识别准确率.考虑不同层特征模式的差异性,在保证识别性能的同时,每层分类前先采用主成分分析法将原始154维特征变换到低维空间,去除冗余特征,加快识别速度.实验证明,所提层次主成分分析法在宫颈细胞分类中相比6种传统多分类方法有更高的识别准确率,可达90％以上;识别速度也较普通层次法提升了21.31％.%In order to recognize multi-class cervical cells automatically,a hierarchical method with PCA ( principal component analysis) feature transformation was proposed and this cell recognition could provide the evidence for cervical cancer diagnosis.The cervical cell recognition was treated as a 4-class classification problem.There were two levels in this hierarchical method.First,one-versus-one strategy was used to train 6 SVM ( support vector machine) classifiers to do a 3-class classification.Second,abnormal cells in one type of 3 categories were classified by a 2-class SVM.To optimize the feature combination and reduce the running time,a feature transformation method named PCA was adopted to transform the original feature vector into low-dimension feature space.The experiments show that the proposed hierarchical PCA recognition method is faster than the common hierarchical method at a ratio of 21.31％,and can distinguish 4 cervical cell categories better than 6 other traditional methods and achieve above 90％ accuracy.
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