The occurrence of false-positives (FPs) is still an important concern and source of unreliability in computer-aided diagnosis systems developed for 3D virtual colonoscopy. This work presents three different supervised approaches, based on supervised artificial neural networks (ANNs) architectures tested on 16 rows helical multi-slice computer tomography. The performance of the best ANN architecture developed, by using the volumes belonging to only 4 of 7 available nodules diagnosed by expert radiologists as polyps and non-polyps were evaluated in terms of FPs and false-negatives. It revealed good performance in terms of generalization and FPs reduction, correctly detecting all 7 polyps. © 2012 Springer-Verlag.
CITATION STYLE
Bevilacqua, V., De Fano, D., Giannini, S., Mastronardi, G., Paradiso, V., Pennini, M., … Moschetta, M. (2011). 3D virtual colonoscopy for polyps detection by supervised artificial neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6840 LNBI, pp. 596–603). https://doi.org/10.1007/978-3-642-24553-4_79
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