Aiming at the problem that the complexity of cervical cell image fine segmentation makes it difficult to achieve automatic abnormal cell screening based on cell image segmentation, a cervical cell classification algorithm without fine segmentation step was proposed. Firstly, a new feature named MAXimum Section (MAXSection) was defined for describing the distribution of pixel values, and was combined with Back Propagation (BP) neural network and Selective Search algorithm to realize the accurate extraction of nucleus Region Of Interest (ROI) (the highest accuracy was 100%). Secondly, two parameters named estimated length and estimated width were defined based on MAXSection to describe morphological changes of abnormal nucleus. Finally, according to the characteristic of absolute enlargement of cervical nucleus when cervical cancer occurs, the classification of abnormal nucleus (at least one parameter of estimated length and width is greater than 65) and normal nucleus (estimated length and width are both less than 65) can be realized by using the above two parameters. Experimental results show that the proposed algorithm has screening accuracy of 98.89%, sensitivity of 98.18%, and specificity of 99.20%. The proposed algorithm can complete the total process from the input of whole Pap smear image to the output of final screening results, realizing the automation of abnormal cervical cell screening.

Existing results of auxiliary word are difficult to use in the automatic annotation of natural language processing. Based on the auxiliary words knowledge base, rule-based method is used in automatic annotation of auxiliary words usage. Contrast to the results of test, it shows that refining, extension and adjusting the matching order of the rules can promote the precision and recall effectively. It is also benefit for improve the quality of Chinese Corpus, deepen the processing depth, and reduce the artificial work.