Application of artificial neural networks for the estimation of tumour characteristics in biological tissues.
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.
Hosseini
Seyed Mohsen
SM
Biomechanics Department, Laboratory of Artificial Tactile Sensing and Robotic Surgery, Faculty of Biomedical Engineering, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran.
Amiri
Mahmood
M
Najarian
Siamak
S
Dargahi
Javad
J
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*