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.
3D Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks / Bevilacqua, V.; De Fano, D.; Giannini, S.; Mastronardi, G.; Paradiso, V.; Pennini, M.; Piccinni, M.; Angelelli, G.; Moschetta, M.. - STAMPA. - 6840:(2012), pp. 596-603. [10.1007/978-3-642-24553-4_79]
3D Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks
Bevilacqua, V.;Giannini, S.;Mastronardi, G.;
2012-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.