title: Application of artificial neural networks for the estimation of tumour characteristics in biological tissues.
abstract: BACKGROUND
  Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce 'forward results'.
  
  
  METHODS
  Three feed-forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back-propagation (RP) algorithm with a leave-one-out (LOO) cross-validation approach is used for training purposes.
  
  
  RESULTS
  The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
  
  
  CONCLUSIONS
  There is a non-linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small.
authors.affiliation: Biomechanics Department, Laboratory of Artificial Tactile Sensing and Robotic Surgery, Faculty of Biomedical Engineering, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran.
authors.forename: Seyed Mohsen
authors.initials: SM
authors.lastname: Hosseini
authors.forename: Mahmood
authors.initials: M
authors.lastname: Amiri
authors.forename: Siamak
authors.initials: S
authors.lastname: Najarian
authors.forename: Javad
authors.initials: J
authors.lastname: Dargahi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000175:diagnosis* / Q000503:physiopathology*; D016571:Neural Networks, Computer; D010173:Palpation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*