Diagnosis of brain tumor using PNN neural networks

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

elahe alipoor azar ¹ , Nasser Lotfivand ²

1 - Islamic Azad Unversity of Tabriz
2 - Islamic Azad University Tabriz Branch · Department of Electronic Engineering

Received: 2018-03-11 Accepted : 2018-06-26 Published : 2018-06-01

Keywords: image segmentation, Keywords: Brain MR, learning vector quantization, self-organizing feature map, stationary wavelet transform,

Abstract :

Cells grow and then need a very neat method to create new cells that work properly to maintain the health of the body. When the ability to control the growth of the cells is lost, they are unconsidered and often divided without order. Exemplified cells form a tissue mass called the tumor. In fact, brain tumors are abnormal and uncontrolled cell proliferations. Segmentation methods are used in biomedical image processing and examines the methods used for better segmentation. Critical assessment of the current state of the automated and automated methods for categorizing anatomical medical pictures with emphasis on the benefits and disadvantages. In this project, we recognize brain tumors and classify tumor stages using database testing and training. Segmentation is used for testing purpose by FCM space. Neural networks are also used for its segmentation, which yields acceptable results in PNN neural networks.