Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification

Pemodelan Segmentasi Sel Epitel Serviks Pada Citra Digital PAP SMEAR

Cervical cancer is a disease that is very deadly. The level of this disease in Indonesia is very high, the rate of prevalence in 2013 of about 0.8% with an estimated 92 692 absolute throughout Indonesia. Early detection of cancer can be done with a Pap cytology examination. Slide readings performed by a specialist in anatomical pathology where normal conditions at least there should be 8000-12000 cells in good condition. This reading is not easy and has some constraints, it is necessary to melakkan designed system capable of automatically reading slide. System automation is expected to reduce errors due to manual readings. One of the stages in the building automation system is the segmentation of the cell to separate the cells from background objects. Separation is done by implementing the method of minima and maxima region, as well as the implementation of a subset of the region area. From this study, it was found that the model is implemented showed that the implementation of the model can be used to do the segmentation process either cell cytoplasm and nucleus