Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 15, 2024

Abstract

BACKGROUND AND OBJECTIVE: The central organ of the human nervous system is the brain, which receives and sends stimuli to the various parts of the body to engage in daily activities. Uncontrolled growth of brain cells can result in tumors which affect the normal functions of healthy brain cells. An automatic reliable technique for detecting tumors is imperative to assist medical practitioners in the timely diagnosis of patients. Although machine learning models are being used, with minimal data availability to train, development of low-order based models integrated with machine learning are a tool for reliable detection.

Authors

  • Rita Appiah
    School of Nuclear Engineering, Purdue University, West Lafayette, IN 47906, USA. Electronic address: arita@purdue.edu.
  • Venkatesh Pulletikurthi
    School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA. Electronic address: vpulleti@purdue.edu.
  • Helber Antonio Esquivel-Puentes
    Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47906, USA.
  • Cristiano Cabrera
    School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA.
  • Nahian I Hasan
    School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA.
  • Suranga Dharmarathne
    R.B. Annis School of Engineering, University of Indianapolis, Indianapolis, IN 46227, USA.
  • Luis J Gomez
  • Luciano Castillo
    School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA.

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