Breast Fine Needle Tumor Classification using Neural Networks

The purpose of this paper is to develop an intelligent diagnosis system for breast cancer classification. Artificial Neural Networks and Support Vector Machines were being developed to classify the benign and malignant of breast tumor in fine needle aspiration cytology. First the features were extracted from 92 FNAC image. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used namely multilayer perceptron (MLP) using back-propagation algorithm, probabilistic neural networks (PNN), learning vector quantization (LVQ) and support vector machine (SVM). The classification results were obtained using 10-fold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity and specificity. The method was evaluated using six different datasets including four datasets related to our work and two other benchmark datasets for comparison. The optimum network for classification of breast cancer cells was found using probabilistic neural networks. This is followed in order by support vector machine, learning vector quantization and multilayer perceptron. The results showed that the predictive ability of probabilistic neural networks and support vector machine are stronger than the others in all evaluated datasets.