Particle Swarm Optimization and Texture Analysis Image Processing Techniques using MRI Images to Detect Brain Tumor in Human
Lalitha R Naik
Lalitha R Naik, Assistant Professor, Dept. of Compter Science, Karnatak Science College, Dharwad, Karnataka, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1152-1159 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2750039520/2020©BEIESP | DOI: 10.35940/ijitee.E2750.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cosmetics and classification findings are the most difficult and powerful task of preparing. MRI (Magnetic Resonance Imaging) is a treatment, which is often used by the radio translator to represent the appearance of a person with no surgery. MRI provides much information on the human body’s body, which helps to control the brain’s brain. MRI is used for research on high resolution, speed of availability, and high profile profile for patients [19]. The deep part of the MRI shape is primarily responsible for the termination of the brain’s brain from the computer that supports medical devices. This book focuses on planning the best way and the best way to diagnose MRI’s brain detection if it supports brain arrest if its focus is on surveillance its vision is: benign or operated by using the SVM configuration process. The method we recommend is to create a configuration using the history and management of the process that will create a split by using a test feature (PSO), extracting compression using GLCM, reducing PCA features, to reduce the feature is also used by ICA (Self-Exam) to provide free access for the GLCM and for SVM format. The result is MATLAB2015.
Keywords: SVM, Performance Matrices, Accuracy, PCA, ICA, Feature Reduction, GLCM
Scope of the Article: Swarm intelligence