On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Classification for Imprecise Environments
Machine Learning
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
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This paper describes a system for automatic detection of pulmonary nodules in lung CT (Computed Tomography) images. After modelling the activity of a single radiologist as two subsequent phases, namely, the regions of interest (ROIs) detection phase and the nodule detection phase, we built a system which emulates a team of radiologists. This is achieved by providing a further phase of collaboration and opinion exchange among the experts at the end of each of the previous phases. We also present experimental results, based on the ROC convex hull method, which show how the team of radiologists obtains better performance than the single best radiologist in both phases. In particular, we achieved a sensitivity of 92.48% against a specificity of about 83.54% in the nodule detection phase.