Application of artificial neural networks for the estimation of tumour characteristics in biological tissues

✍ Scribed by Seyed Mohsen Hosseini; Mahmood Amiri; Siamak Najarian; Javad Dargahi

• Publisher: Wiley (Robotic Publications)
• Year: 2007
• Tongue: English
• Weight: 650 KB
• Volume: 3
• Category: Article
• ISSN: 1478-5951
• DOI: 10.1002/rcs.138

✦ Synopsis

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.

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.