Applications of neural networks in biomedical engineering

2002 ◽  
Author(s):  
E. Micheli-Tzanakou
Keyword(s):  
Neural Networks ◽  
Biomedical Engineering

Deep Learning and Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 562-575
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

scholarly journals Extraction of Load Information From Photoelastic Images Using Neural Networks

2006 ◽  
Author(s):  
Venketesh N. Dubey ◽  
Gurtej S. Grewal ◽  
Denzil J. Claremont
Keyword(s):  
Neural Networks ◽  
Finite Element Analysis ◽  
Biomedical Engineering ◽  
Stress Pattern ◽  
Field Analysis ◽  
Element Analysis ◽  
Sensing Applications ◽  
Fringe Patterns ◽  
Theoretical Results ◽  
And Robotics

Photoelastic materials develop colored fringes under white light when subjected to mechanical stresses which can be viewed through a polariscope. This technique has traditionally been used for stress analysis of loaded components, however, this can also be potentially used in sensing applications where the requirement may be measurement of the stimulating forces causing the generation of the fringes. This leads to inverse photoelastic problem where the developed image can be analyzed for the input forces. However, there could be infinite number of possible solutions which cannot be obtained by conventional techniques. This paper presents neural networks based approach to solve this problem. Experiments conducted to prove the principle have been verified with theoretical results and finite element analysis of the loaded specimens. The technique, if fully developed, can be implemented for any generalized case involving complex fringe patterns under different loading conditions for whole-field analysis of the stress pattern, which may find application in a variety of specialized areas including biomedical engineering and robotics.

Deep Learning and Biomedical Engineering

2018 ◽  
pp. 283-296
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

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