Intelligent classification system for cancer data based on artificial neural network

Abstract

This paper describes an intelligent classification system for cancer data. The system employs a hybrid radial basis function (HRBF) network in order to classify cancer data into several classes. The HRBF network is trained using the moving k-means clustering algorithm to position the network's centre and the Given least square (GLS) algorithm to estimate the network's weights. Two cancer data, i.e. cervical cancer and breast cancer, are used as case studies. For cervical cancer, the system classifies the data into three classes, i.e. normal, low grade squamous intraepithclial lesion (LSIL) and high grade squamous intraepithclial lesion (HSIL). The system produces 98.00% accuracy. While for breast cancer, the system classifies the data into benign and malignant data. The system produces 98.57% accuracy. The result illustrates the promising capabilities of the system for assisting cervical and breast cancer detection.