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.

Autore Pugliese

• Bevilacqua Vitoantonio , Mastronardi Giuseppe

Tutti gli autori

• Bevilacqua, V , De Fano, D , Giannini, S , Mastronardi, G , Paradiso, V , Pennini, M , Piccinni, M , Angelelli, G , Moschetta, M

Titolo volume/Rivista

LECTURE NOTES IN COMPUTER SCIENCE

Anno di pubblicazione

2012

ISSN

1611-3349

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile