Artificial Tactile Sensing and Robotic Surgery Using Higher Order Neural Networks

Abstract

In this chapter, a new medical instrument, namely, the Tactile Tumor Detector (TTD) able to simulate the sense of touch in clinical and surgical applications is introduced. All theoretical and experimental attempts for its construction are presented. Theoretical analyses are mostly based on finite element method (FEM), artificial neural networks (ANN), and higher order neural networks (HONN). The TTD is used for detecting abnormal masses in biological tissue, specifically for breast examinations. We also present a research work on ANN and HONN done on the theoretical results of the TTD to reduce the subjectivity of estimation in diagnosing tumor characteristics. We used HONN as a stronger open box intelligent unit than traditional black box neural networks (NN) for estimating the characteristics of tumor and tissue. The results show that by having an HONN model of our nonlinear input-output mapping, there are many advantages compared with ANN model, including faster running for new data, lesser RMS error and better fitting properties.