Date of Publication :17th August 2017

Abstract: A Brain Cancer is very serious disease causing deaths of many individuals. The detection and classification system must be available so that it can be diagnosed at early stages. Cancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells’ morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively or not. This paper deals with such a system which uses computer based procedures to detect tumour blocks and classify the type of tumour using Artificial Neural Network Algorithm for MRI images of different patients. Different image processing techniques such as image segmentation, image enhancement and feature extraction are used for detection of the brain tumour in the MRI images of the cancer affected patients. Detecting Brain tumour using Image Processing techniques involves four stages namely Image Pre-Processing, Image segmentation, Feature Extraction, and Classification. Image processing and neural network techniques are used to improve the performance of detecting and classifying brain tumour in MRI images. MRI scan images are taken for this project to process it.This work presents the artificial neural network approach. It is used to classify the type of tumor in MRI images. The whole system worked in two modes firstly Training/Learning mode and secondly Testing/Recognition mode finally gets a classified output. This paper gives an overview of image segmentation technique based on Particle Swarm Optimization (PSO). PSO is one of the latest and emerging digital image segmentation techniques inspired from the nature. After segmentation features are extracted and submitted to a kernel support vector machine (KSVM).

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