Improving the Medical Diagnosis Process Using Maximal Association Rules

Abstract

In this paper, methods based on regular association rules and improved by maximal association rules are developed for discovering interesting associations between medical images and diagnoses. The proposed methods have important characteristics that make them different from other computer assisted diagnosis methods: the process is automatic, having the possibility to define a great number of diagnoses; the methods could be applied to any medical domain, because the visual features, the semantic indicators remain unchangeable, and the semantic rules are generated by learning from labeled images-examples; the selection of the visual characteristics set is based on their retrieval accuracy; the spatial information of the regions is considered, offering important medical information of the relationships between sick regions. Although we present the results achieved in endoscopic images analysis, our methods can be used to analyze other types of medical images.