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Application of artificial neural networks for the estimation of tumour characteristics in biological tissues
Author(s) -
Hosseini Seyed Mohsen,
Amiri Mahmood,
Najarian Siamak,
Dargahi Javad
Publication year - 2007
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.138
Subject(s) - computer science , artificial neural network , tumour tissue , stiffness , inverse , artificial intelligence , backpropagation , pattern recognition (psychology) , biomedical engineering , mathematics , pathology , materials science , medicine , geometry , composite material
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. Conclusion 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. Copyright © 2007 John Wiley & Sons, Ltd.