Medical Image Classification Using MRI: An Investigation

  • R. MerjulahEmail author
  • J. Chandra
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


The main objective of the paper is to review the performance of various machine learning classification technique currently used for magnetic resonance imaging. The prerequisite for the best classification technique is the main drive for the paper. In magnetic resonance imaging, detection of various diseases might be simple but the physicians need quantification for further treatment. So, the machine learning along with digital image processing aids for the diagnosis of the diseases and synergizes between the computer and the radiologist. The review of machine learning classification based on the support vector machine, discrete wavelet transform, artificial neural network, and principal component analysis reveals that discrete wavelet transform combined with other highly used method like PCA, ANN, etc., will bring high accuracy rate of 100%. The hybrid technique provides the second opinion to the radiologist on taking the decision.


Magnetic resonance imaging (MRI) Support vector machine (SVM) Discrete wavelet transform (DWT) Artificial neural network (ANN) Principal component analysis (PCA) 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Christ UniversityBangaloreIndia

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