This paper presents a method for breast cancer diagnosis using a system based on an artificial neural network (ANN) trained using a particular version of the back propagation (BP) algorithm. The Wisconsin Breast Cancer Database (WBCD) was used in order to train and validate the ANN; WBCD is composed by 699 cases monitored by Doc. William H. Wolberg in the first '90s. The development of this system required an articulate phase of data analysis and preprocessing: various statistical tools like principal component analysis (PCA) and principal factor analysis (PFA), were used in order to find parameters more strictly correlated to the malignant/benignant nature of the cancer. Non linear data analysis techniques were employed to gain more knowledge about the internal structure of the database. A genetic algorithm was then set up to find the best topology of ANN. The analysis of results obtained by IDEST followed training and validation of the ANN

Hybrid data analysis methods and artificial neural network design in breast cancer diagnosis: IDEST experience / Bevilacqua, Vitoantonio; Mastronardi, Giuseppe; Menolascina, F.. - (2006), pp. 373-378. (Intervento presentato al convegno International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, CIMCA-IAWTIC tenutosi a Vienna, Austria nel November 28-30, 2005) [10.1109/CIMCA.2005.1631497].

Hybrid data analysis methods and artificial neural network design in breast cancer diagnosis: IDEST experience

BEVILACQUA, Vitoantonio;MASTRONARDI, Giuseppe;
2006-01-01

Abstract

This paper presents a method for breast cancer diagnosis using a system based on an artificial neural network (ANN) trained using a particular version of the back propagation (BP) algorithm. The Wisconsin Breast Cancer Database (WBCD) was used in order to train and validate the ANN; WBCD is composed by 699 cases monitored by Doc. William H. Wolberg in the first '90s. The development of this system required an articulate phase of data analysis and preprocessing: various statistical tools like principal component analysis (PCA) and principal factor analysis (PFA), were used in order to find parameters more strictly correlated to the malignant/benignant nature of the cancer. Non linear data analysis techniques were employed to gain more knowledge about the internal structure of the database. A genetic algorithm was then set up to find the best topology of ANN. The analysis of results obtained by IDEST followed training and validation of the ANN
2006
International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, CIMCA-IAWTIC
0-7695-2504-0
Hybrid data analysis methods and artificial neural network design in breast cancer diagnosis: IDEST experience / Bevilacqua, Vitoantonio; Mastronardi, Giuseppe; Menolascina, F.. - (2006), pp. 373-378. (Intervento presentato al convegno International Conference on Computational Intelligence for Modelling Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, CIMCA-IAWTIC tenutosi a Vienna, Austria nel November 28-30, 2005) [10.1109/CIMCA.2005.1631497].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/21162
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact