Cervical cell image inspection—a task for artificial neural networks

Abstract

The author describes work conducted using artificial neural networks, in the form of a multilayer perceptron, employing error backpropagation and trained on a database of features derived from 524 expertly classified single cervical cells and subsequently tested on a further 524 previously unseen cells. Pre-processing of the data was used to achieve a data reduction of better than 99%. Each cell image was converted from its 256*256 pixel format to its frequency spectrum from which 80 features containing texture and energy information were extracted. The artificial neural network was trained and tested using this compressed data representation. The performance of a number of different network arrangements was investigated. The best results were obtained using a network with 80 inputs, 4 processing elements in a single hidden layer and 1 processing element in the output layer. The network with this arrangement was able to correctly classify, as either normal or abnormal, 98% of the cell images in the training set and 96% of the cell images in the test set.