A multiple expert system for classifying fluorescent intensity in antinuclear autoantibodies analysis

Abstract

At the present, Indirect Immunofluorescence (IIF) is the recommended method for the detection of antinuclear autoantibodies (ANA). IIF diagnosis requires both the estimation of the fluorescent intensity and the description of the staining pattern, 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 (CAD) tools that can offer a support to physician decision. In this paper we first propose a strategy to reliably label the image data set by using the diagnoses performed by different physicians, and then we present a system to classify the fluorescent intensity. Such a system adopts a multiple expert system architecture (MES), based on the classifier selection paradigm. Two different selection rules are presented and, given the application domain, the convenience of using one of them is analyzed. Different sets of operating points are determined, making the recognition system suited to application in daily practice and in a wide spectrum of scenarios. The measured performance on an annotated database of IIF images shows a low overall miss rate (<1.5%, 0.00% of false negative).