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.
|Titolo:||3D Virtual Colonoscopy for Polyps Detection by Supervised Artificial Neural Networks|
|Titolo del libro:||ICIC (3) [Bio-Inspired Computing and Applications|
|Data di pubblicazione:||2012|
|Digital Object Identifier (DOI):||10.1007/978-3-642-24553-4_79|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|