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In this study, the analysis of automatic diagnosis of pap smear image extraction using neural network algorithm, the analysis includes a review of the results of pap smear Herlev image extraction with level 7 classes, 2 normal and abnormal classes, 3 normal level dysplasia classes and 4 abnormal level dysplasia classes. The problem is that the neural network is very difficult to designate optimal features in diagnosis and it is difficult to deal with class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize bagging features and methods to deal with class imbalances, with the aim that the diagnosis results using a neural network can improve its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the network neural algorithm. At level 7 classes could increase by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 classes increased by 5.47%.

NEURAL NETWORK OPTIMIZATION WITH PARTICLE SWARM OPTIMIZATION AND BAGGING METHODS ON CLASSIFICATION OF SINGLE PAP SMEAR IMAGE CELLS