Computer-aided detection of lung nodules based on decision fusion techniques |
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Authors: | Michela Antonelli Marco Cococcioni Beatrice Lazzerini Francesco Marcelloni |
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Affiliation: | (1) Dipartimento di Ingegneria dell’Informazione: Elettronica, Informatica, Telecomunicazioni, University of Pisa, Largo Lucio Lazzarino, 1, via Diotisalvi 2, 56122 Pisa, Italy; |
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Abstract: | We adopted decision fusion techniques to develop a computer-aided detection (CAD) system for automatic detection of pulmonary
nodules in low-dose CT images. Two distinct phases, aimed, respectively, at detecting volumes of interests (VOIs) within the
CT scan, and at classifying VOIs into nodules and non-nodules, were considered. Three algorithms, namely thresholding, region
growing and robust fuzzy clustering, were used as VOI detectors. For the classification phase, we built multi-classifier systems,
which aggregate the decisions of three statistical classifiers, a neural network and a decision tree. Finally, the receiver
operating characteristic convex hull method was used to build the final classifier, which results to be the aggregation of
the best local behaviors of both classifiers and combiners. All the CAD modules were tested on CT scans analyzed by two expert
radiologists. In the experiments, we achieved a sensitivity of 92.5% against a specificity of 83.5%. |
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