A comparison of neural network architectures for cervical cell classification

The authors investigate the use of various neural network architectures for the analysis and classification of smear scenes. A feature space was derived from the magnitude of the Fourier transform using a wedge-ring arrangement. The features obtained were invariant to translation and rotation. Neural nets were then used to both reduce dimensionality and to perform the classification. An expertly verified database containing over 2000 high-resolution cell images was used to measure the performance of the nets. The single-layer perceptron, multilayer perceptrons and the constructive algorithm of Fahlman and Lebiere were each used as classifiers. The effect of feature extraction nets for pre-processing the feature space was also investigated. Performances were compared in terms of speed, network size and ability to learn and generalise. In addition, classification by a parametric Bayesian classifier allowed comparison with a statistical method. Good classification results were obtained