Performance of Hybrid Radial Basis Function network: Adaptive Fuzzy K-Means versus Moving k-Means Clustering as centre positioning algorithms on cervical cell precancerous stage classification

Cervical cancer been known to be the cause of many deaths each year. Screening tests, such as the Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test does have some disappointing disadvantages such as the fact that it has less effective slide preparation and also that it is laden with human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. Recently, artificial neural networks have been widely implemented as a cervical cancer diagnosis system i.e. to classify cervical cancer into normal and abnormal cells. In this recent study, neural network architecture i.e. the Hybrid Radial Basis Function (HRBF) network with Adaptive Fuzzy K-Means Clustering (AFKM) asa centre positioning algorithm is used to diagnose cervical cancer. Four extracted features of cervical cell are used as input data to the networks, which are the size of nucleus and cytoplasm and its grey level. Cells from normal, Low-grade Squamous Intraepithelial Lesion (LSIL) and High-grade Squamous Intraepithelial Lesion (HSIL) categories are used as the training as well as the testing data. The data are fed randomly into the neural networks via 5-folds cross validation techniques. The network performance is compared with the HRBF network with the Moving K-Means algorithm as the centre positioning algorithm. The proposed network produces better accuracy, sensitivity and specificity which illustrates the promising capability of the network to be implemented as cervical cancer diagnosis system for Pap test performance improvement.