How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method

In this paper we present a new method, called IDEA, which employs association rules to assist in medical image diagnosis. IDEA mines association rules, relating visual features with the knowledge gotten from specialists, and employs the associations to suggest possible diagnoses for a given medical image. IDEA incorporates two new algorithms called Omega and ACE. Omega performs simultaneously feature selection and data discretization very efficiently with linear cost on the number of feature values. ACE is a new associative classifier, which has the particular ability of suggesting multiple keywords to compose the diagnosis for a given medical image. The IDEA method has an important characteristic that makes it different from other CAD methods: it suggests multiple diagnosis hypotheses for an image and ranks them based on a measure of quality. The IDEA method was implemented in a prototype (IDEA system) for radiologists evaluate it. The radiologists showed enormous interest in employing the system to aid them in their daily work. The IDEA system was applied to real datasets and the results presented high accuracy (up to 96.7%). The results testify that association rules are well-suited to support the diagnosing task.