Computing-Inspired Detection of Multiple Cancers

A new computing-inspired multiple-cancer detection procedure (MCDP) is proposed. In the MCDP, the cancer areas to be detected can be regarded as solutions of an objective function, the tissue region around the cancer areas can be mapped to the parameter space of the solutions, and the nanorobots correspond to the agents in the optimization procedure. The process that the nanorobots look for the cancer areas by swimming in the tissue region can be mapped to the process that the agents search for the solutions in the parameter space. Niche Genetic Algorithm (NGA) is widely used in multimodal function optimization and non-monotonic function optimization. It can search all global optimums of multiple hump function in a running, keep the diversity of the population effectively, and avoid premature of solutions got from normal GA. Inspired by the optimization procedure of NGA, the multiple cancer detection procedure (MCDP) has been studied and the NGA-inspired cancer detection procedure has been proposed in order to locate the targets efficiently at the same time by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, some comparative numerical examples are presented to demonstrate the effectiveness of the NGA-inspired MCDP.

[1]  Tung-Kuan Liu,et al.  A Novel Crowding Genetic Algorithm and Its Applications to Manufacturing Robots , 2014, IEEE Transactions on Industrial Informatics.

[2]  Massimiliano Pierobon,et al.  A Molecular Communication System Model for Particulate Drug Delivery Systems , 2013, IEEE Transactions on Biomedical Engineering.

[3]  Athanasios V. Vasilakos,et al.  Green Touchable Nanorobotic Sensor Networks , 2016, IEEE Communications Magazine.

[4]  Berk,et al.  Scale-invariant behavior and vascular network formation in normal and tumor tissue. , 1995, Physical review letters.

[5]  P. S. Anwar,et al.  A Touch-Communication Framework for Drug Delivery Based on a Transient Microbot System , 2015, IEEE Transactions on NanoBioscience.

[6]  Jean-Charles Preiser,et al.  Microvascular blood flow is altered in patients with sepsis. , 2002, American journal of respiratory and critical care medicine.

[7]  J W Baish,et al.  Fractals and cancer. , 2000, Cancer research.

[8]  Jonathan R. Lindner,et al.  Imaging Tumor Angiogenesis With Contrast Ultrasound and Microbubbles Targeted to &agr;v&bgr;3 , 2003 .

[9]  Xin Yao,et al.  Touchable computation: Computing-inspired bio-detection , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[10]  Sylvain Martel,et al.  MRI-based Medical Nanorobotic Platform for the Control of Magnetic Nanoparticles and Flagellated Bacteria for Target Interventions in Human Capillaries , 2009, Int. J. Robotics Res..

[11]  D-S Lee,et al.  Flow correlated percolation during vascular remodeling in growing tumors. , 2005, Physical review letters.

[12]  Marko Seppänen,et al.  Decreased Blood Flow with Increased Metabolic Activity: A Novel Sign of Pancreatic Tumor Aggressiveness , 2009, Clinical Cancer Research.

[13]  S. Martel,et al.  Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions , 2016, Nature nanotechnology.

[14]  A. Vasilakos,et al.  Molecular Communication and Networking: Opportunities and Challenges , 2012, IEEE Transactions on NanoBioscience.

[15]  Robert R. Bies,et al.  A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection , 2006, Journal of Pharmacokinetics and Pharmacodynamics.

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .