The task of cervical cell classification can be divided into four sub-tasks: (1) the isolation of single cells, cell clusters and clumps as well as artifacts, (2) the segmentation of the cell image into nucleus and cytoplasm, (3) the extraction of cell features such as size and density of the nucleus and cytoplasm, grey level extrema, fractal dimension, texture parameters and shape measures, and (4) the use of these features to classify the cell as normal or abnormal. The final problem of formulating a diagnostic decision based on these data is a multivariate statistical one, to which there are many theoretical and practical solutions. Palcic et al. (1992) have performed a discriminant function analysis of a large set of such measurements, and have achieved a high predictive accuracy. This paper describes a solution for the cell classification task which utilizes a hierarchical system of artificial neural networks (ANNs) using backpropagation (BP) and achieves extremely high accuracy

An application in the area of chemical and biosensor design has provided the inspiration for research into some of the issues involved with the design and application of modular artificial neural networks (ANNs) for pattern classification tasks. We can divide the development of modular ANNs into two main components: (1) the topological design of the individual modular ANNs and the construction of the assembly of modules; and (2) the analysis of the data sets to be used to train the individual modules. The chemical sensor design task allows us to explore this second component to identify some of the implications for the capture and analysis of data appropriate for the training of modular ANN systems.< >