An Approach to Brain Tumor Segmentation and Severity Analysis using Particle Swarm Optimization
Divyanshu Sinha1, Aditya Tandon2, Phong Thanh Nguyen3, S. Rama Sree4

Manuscript received on 15 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 2102-2106 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F13770476S519/2019©BEIESP
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Abstract: Medical image processing is one of the most challenging and emerging filed. Processing of medical image is one of the important tasks for the diagnosis of brain tumor. Image segmentation is required for detection of brain tumors, which is a quite complicated job if performed automatically. In recent time, scientists from various fields including medical, mathematical and computer science have collaborated together to find out a better understanding of the disease and devise more cost effective treatments. Due to advancements in the field of science and technology ,we have innumerous methods for image segmentation which are used for the detection of brain tumor and to clearly recognize it from MRI imagery. Various methods and algorithms have been implemented for segmenting MRI imagery. In the following paper, we have used Particle Swarm Optimization (PSO) technique to recognize brain tumor by looking at an MRI image. For severity analysis of brain tumor, machine learning algorithm is used.
Keywords: Magnetic Resonance Imaging (MRI), Brain Tumor, Particle Swarm Optimization, Machine Learning.
Scope of the Article: Swarm Intelligence