Brain Tumor Detection from MRI Images Using Optimization Segmentation Techniques

Abstract

The segmentation of medical images is a dynamic and relaxing task because of the distinct features of the images. It is one of the software needed for brain image segmentation to identify the brain tumor and will also be applied to the pre-processing stage to improve detection accuracy. Brain segmentation for magnetic resonance imaging (MRI) is a challenging task given the high-quality Picture quality, anatomical brain structures and tumor complexity. Several researchers have developed several algorithms around tumor segmentation in the field of medical imaging. This work contrasts the effects of optimization with Particle Swam Optimization (PSO) segmentation technique and Darwinian Particle Swam Optimization (DPSO) and Functional Order Darwinian Particle Swam Optimization (FO-DPSO). Using these three chosen methods, the accuracy of the collected data set has been measured and the findings are addressed, the paper from these. We validate these results with the PSO, DPSO and FOPSO, which are commonly used. The method used in the study of medical images. The future work will measure the size of the expected tumor area, which will give the algorithms more accuracy.