Paper Info
Title
A modular extreme learning machine with linguistic interpreter and accelerated chaotic distributor for evaluating the safety of robot maneuvers in laparoscopic surgery.
Abstract
In this investigation, a systematic sequential intelligent system is proposed to provide the surgeon with an estimation of the state of the tool-tissue interaction force in laparoscopic surgery. To train the proposed intelligent system, a 3D model of an in vivo porcine liver was built for different probing tasks. To capture the required knowledge, three different geometric features, i.e. Y displacement of the nodes on the upper surface and slopes on the closest node to the deforming area of the upper edge in both X–Y and Z–Y planes, were extracted experimentally. The numerical simulations are conducted in three independent successive stages. At the first step, a well-known partition-based clustering technique called accelerated chaotic particle swarm optimization (ACPSO) is used to cluster the information of database into a number of partitions. Thereafter, a modular extreme learning machine (M-ELM) is used to model the characteristics of each cluster. Finally, the output of M-ELM is fed to a Mamdani fuzzy inference system (MFIS) to interpret the safety of robot maneuvers in laparoscopic surgery. The proposed intelligent framework is used for real-time applications so that the surgeon can adjust the movements of the robot to avoid operational hazards. Based on a rigor comparative study, it is indicated that not only the proposed intelligent technique can effectively handle the considered problem but also is a reliable alternative to physical sensors and measurement tools.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.10.003
Neurocomputing
Keywords
Field
DocType
Laparoscopic surgery,Medical robotics,Soft tissue modeling,Clustering,System identification,Fuzzy inference system
Laparoscopic surgery,Simulation,Extreme learning machine,Computer science,Interpreter,Artificial intelligence,Modular design,System identification,Chaotic,Cluster analysis,Robot,Machine learning
Journal
Volume
ISSN
Citations 
151
0925-2312
5
PageRank 
References 
Authors
0.42
26
2
Name
Order
Citations
PageRank
Ahmad Mozaffari128024.01
Saeed Behzadipour2698.16