In the current work, the effect of implementing a medical image data-set query application on the grid is studied. Medical imaging is extremely data intensive, because of the size of medical image scans. Grids offer immense processing power as well as a possibility for great levels of coarse grain parallelism adequate for tackling queries on medical image datasets in a comparatively shorter time period. Apart from the security issues, which are common in the domain, the possible parallelism of grids is challenging to make use of. In the current study, the max–min method, Genetic Algorithm (GA), wherein genetic material is substituted by strings of bits while natural selection is substituted by fitness functions, Particle Swarm Optimization (PSO), wherein all particles utilize their own memories and optimum solutions are discovered on the basis of the knowledge obtained by the swarm as a whole as well as a modified PSO (PSO with 2-opt algorithms) are suggested. The outcomes of experiments proved that modified PSO outperformed max–min, GA as well as PSO.
- Genetic Algorithm (GA)
- grid computing
- Medical image database
- Particle Swarm Optimization (PSO)