Development of a Computerized Diagnostic System for Brain MRI Tumor Scanning Using a Robust Information Clustering Technique

Abstract

This paper presents the development of a computerized diagnostic system for brain MRI tumor scanning using a robust information clustering technique. The method used for this study is data collection, data processing, feature extraction, artificial neural network, activation function, training algorithm, and classification. The method was modelled using a structural approach that developed the Artificial Neural Network (ANN) algorithm, using tansig activation function and back-propagation training algorithm. The brain tumor detection algorithm developed was implemented with MATLAB Simulink application, and tested with Mean Square Error (MSE) and Regression (R) analysis. The result showed that the MSE is 0.002488 and the Regression result is 0.9933. The algorithm was also comparatively compared with an existing system and the result showed that the new system achieved better regression performance than the others. Then it was deployed as a clinical decision system for the diagnosis of brain tumors and tested, the result showed that it was able to detect patients with brain MRI data. Keyword : Back-Propagation, Magnetic Resonance Imaging (MRI), Neural Network, Simulink, Tansig.