A Multi-Expert System to Classify Fluorescent Intensity in Antinuclear Autoantibodies Testing

摘要

Indirect Immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detection. IIF diagnosis requires estimating fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer aided diagnosis tools that can offer a support to physician decision. In this paper we propose a system to classify the fluorescent intensity: initially we discuss two classifiers based on Artificial Neural Networks that can recognize intrinsically dubious samples and whose error tolerance can be flexibly set according to a given rule. Since such classifiers complement one other, we adopt a Multiple Expert System that aggregates the two experts. The final decision of the system results from the combination of the outputs of the single experts. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice.