A Hybrid Particle Swarm and Neural Network Approach for Detection of Prostate Cancer from Benign Hyperplasia of Prostate.
In present paper, we propose a Hybrid classifier based particle swarm optimization (PSO) and Neural Network method for supporting the diagnosis of prostate cancer. algorithm combining particle swarm optimization algorithm with back propagation neural network (BPNN) algorithm, also referred to as BPN...
| Publicado en: | Studies in Health Technology & Informatics Vol. 205; pp. 481 - 486 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2014
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116234629&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116234629 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2014 vid: 205 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 116234629 116234629 116234629 10.3233/978-1-61499-432-9-481 116234629 ppf: 481 ppct: 5 formats: tig: atl: A Hybrid Particle Swarm and Neural Network Approach for Detection of Prostate Cancer from Benign Hyperplasia of Prostate. aug: au: SADOUGHI, Farhnaz GHADERZADEH, Mustafa affil: School of Health Management & Information Sciences. Iran University of Medical Sciences sug: subj: Prostate Pathology Neural Networks (Computer) Artificial Intelligence Prostatic Neoplasms Algorithms Diagnosis Methods Neoplasms Diagnosis ab: In present paper, we propose a Hybrid classifier based particle swarm optimization (PSO) and Neural Network method for supporting the diagnosis of prostate cancer. algorithm combining particle swarm optimization algorithm with back propagation neural network (BPNN) algorithm, also referred to as BPNN- PSO algorithm, is proposed to train the feed forward neural network (FNN). The results show that the proposed BP based PSO algorithm can achieve very high diagnosis accuracy (98%) and it proving its usefulness in support of clinical decision process of prostate cancer. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|