Brain Tumor Image Classification and Grading Using Convolutional Neural Network and Particle Swarm Optimization Algorithm
N. Hema Rajini
N. Hema Rajini, Department of Computer Science and Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi (Tamil Nadu), India.
Manuscript received on 25 May 2019 | Revised Manuscript received on 03 June 2019 | Manuscript Published on 22 June 2019 | PP: 42-48 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C10100283S19/19©BEIESP
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Abstract: Brain tumor defines the aggregation of abnormal cells in certain tissues of the brain area. The earlier identification of brain tumor plays a significant part in the treatment and recovery of the patient. The identification of a brain tumor and its grade is generally a difficult and time consuming task. For effective classification and grading of brain tumor images, in this paper, we present a convolutional neural network (CNN) and particle swarm optimization (PSO) algorithm of Glioma by the use of magnetic resonance imaging (MRI). The presented CNN-PSO model make use of PSO algorithm to select the deep neural network architecture which are generally depends on trial and error or by employed fixed structures. A detailed experimentation of the CNN-PSO method is carried out on several benchmark MRI brain images and verified its effectiveness on the applied test images with respect to different classification measures.
Keywords: Brain Tumor, Cnn, Deep Learning, Particle Swarm Optimization.
Scope of the Article: Discrete Optimization