Brain Tumor Detection Using Neural Network
Pankaj Sapra1, Rupinderpal Singh2, Shivani Khurana3
1Er.Pankaj Sapra, Computer Science, Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab), India.
2Er.Rupinderpal Singh, Computer Science, Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab), India.
3Prof. Shivani Khurana, Computer Science, Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab), India.
Manuscript received on August 05, 2013. | Revised Manuscript received on August 11, 2013. | Manuscript published on August 15, 2013. | PP: 83-88 | Volume-1 Issue-9, August 2013. | Retrieval Number: : I0425081913/2013©BEIESP
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© The Authors. Published By: 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: The segmentation of brain tumors in magnetic resonance images (MRI) is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI) used in different stages of Computer Aided Detection System (CAD). Brain Image classification techniques are studied. Existing methods are classically divided into region based and contour based methods. These are usually dedicated to full enhanced tumors or specific types of tumors. The amount of resources required to describe large set of data is simplified and selected in for tissue segmentation. In this paper, modified image segmentation techniques were applied on MRI scan images in order to detect brain tumors. Also in this paper, a modified Probabilistic Neural Network (PNN) model that is based on learning vector quantization (LVQ) with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification using MRI-scans. The assessment of the modified PNN classifier performance is measured in terms of the training performance, classification accuracies and computational time. The simulation results showed that the modified PNN gives rapid and accurate classification compared with the image processing and published conventional PNN techniques. Simulation results also showed that the proposed system out performs the corresponding PNN system presented and successfully handle the process of brain tumor classification in MRI image with 100% accuracy.
Keywords: Magnetic Resonance Image (MRI),Computer Aided Detection System (CAD), Probabilistic Neural Network (PNN), Edge detection.