Cover
Vol. 16 No. 2 (2020)

Published: December 31, 2020

Pages: 58-67

Original Article

Automated Brain Tumor Detection Based on Feature Extraction from The MRI Brain Image Analysis

Ban Mohammed Abd Alreda 1 Hussain Kareem Khalif Thamir Rashed Saeid
1Electrical Engineering Department, University of Technology, Iraq
History
  • Received: October 1, 2020
  • Revised: October 14, 2020
  • Accepted: October 16, 2020
  • Available Online: October 16, 2020
DOI

https://doi.org/10.37917/ijeee.16.2.6

Abstract

The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).

Keywords

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