BRAIN STROKE DETECTION USING TENSOR FACTORIZATION AND MACHINE LEARNING MODELS

  • Mosaad W. Hassan Department of mathematics (computer science), faculty of science, Tanta University, Egypt. https://orcid.org/0000-0002-9282-2049
  • Arabi Keshk Faculty of computers and information, Menofia University, Egypt.
  • Amira Abd El-atey Faculty of computers and information, Menofia University, Egypt.
  • Elham Alfeky faculty of science tanta university https://orcid.org/0000-0002-7098-2949
Keywords: Brain Stroke, Tensor Factorization, Classification, Machine Learning, SVM.

Abstract

Stroke is one of the foremost common disorders among the elderly. Early detection of stroke from Magnetic Resonance Imaging (MRI) is typically based on the representation method of these images. Representing MRI slices in two dimensional structures (matrices) implies ignoring the dependencies between these slices. Additionally, to combine all features exist in these slices requires more computations and time. However, this results in inexact diagnosis. In this paper, we propose a new tensor-based approach for stroke detection from MRI. The proposed methodology has two phases. In first phase, each patient’s MRI are represented as a tensor. Tensor representations are powerful because they capture the dependencies in high-dimensional data, MRI of patient, which gives more reliable and accurate results. Also, tensor factorization is used as a method for feature extraction and reduction, which improves the performance and accuracy of classifiers. In second phase, these extracted features are used to train support vector machine (SVM) and XGBoost classifiers to classify MRI images into normal and abnormal. The proposed method is assessed with MRI dataset, and the conducted experiments illustrate the efficiency of this approach. It achieves classification accuracy of 98%.

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Published
2021-08-16
How to Cite
Hassan, M. W., Keshk, A., El-atey, A. A., & Alfeky, E. (2021). BRAIN STROKE DETECTION USING TENSOR FACTORIZATION AND MACHINE LEARNING MODELS. International Journal of Engineering Technologies and Management Research, 8(8), 1-12. https://doi.org/10.29121/ijetmr.v8.i8.2021.1006