Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by Computer-Aided Diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, the authors present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data).