Comparison of neural network and fuzzy c-means methods in bladder cancer cell classification

We report the performances of cancer cell classification by using supervised and unsupervised learning techniques. A single hidden layer feed-forward NN with error back-propagation training is adopted for supervised learning, and c-means clustering methods, fuzzy and non-fuzzy, are used for unsupervised learning. Network configurations with various activation functions, namely sigmoid. sinusoid and gaussian, are studied. A set of features, including cell size, average intensity, texture, shape factor and pgDNA are selected as the input for the network. These features, in particular the texture information, are shown to be very effective in capturing the discriminate information in cancer cells. It is found, based on the data from 467 cell images from six cases, the neural network approach achieves a classification rate of 96.9% while fuzzy c-means scores 76.5%.