International Journal of Computational Intelligence, Biotechnology and Biochemical Engineering

Authors: V. Gupta, R. Sharma, S. Kaur

Abstract: In this paper we propose a system which uses computer-based procedures to detect tumor blocks and classify the type of tumor using artificial neural networks for Magnetic Response Imaging (MRI) images of different patients. Here, we present a system to classify the given MRI brain images as normal or abnormal. The proposed system employs wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. Different image processing techniques such as image segmentation, image enhancement and feature extraction are used for detection of the brain tumor in the MRI images of the cancer affected patients. Medical image processing is the fast growing and challenging field nowadays. Detecting brain tumor using image processing techniques involves four stages namely image preprocessing, image segmentation, feature extraction, and classification. Image processing and neural network techniques are used to improve the performance of training and classifying brain tumor in MRI images. It is expected that the experimental results of the proposed system will give better result in comparison to other existing systems.