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Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep

Muhammad Arif1, F Ajesh2, Shermin Shamsudheen3

  • 1Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan.

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This study introduces a novel brain tumor detection system using magnetic resonance imaging (MRI) and deep learning. The method accurately segments and classifies tumors, addressing the shortage of expert radiologists.

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Area of Science:

  • Medical Imaging
  • Biomedical Image Processing
  • Radiology

Background:

  • Accurate tumor identification in radiology requires extensive medical knowledge, highlighting a need for automated detection systems.
  • Biomedical image processing of magnetic resonance imaging (MRI) aids in detecting and locating brain tumors, though challenges exist due to tissue variability.
  • The scarcity of qualified radiologists underscores the importance of developing advanced tumor detection programs.

Purpose of the Study:

  • To develop an automated system for brain tumor segmentation and detection using MRI images.
  • To classify brain scans as either healthy or containing a tumor.
  • To enhance the performance and simplify medical image segmentation for brain tumors.

Main Methods:

  • Utilized Berkeley's Wavelet Transformation (BWT) and a deep learning classifier for image segmentation and detection.
  • Employed the Gray-Level Co-occurrence Matrix (GLCM) for extracting significant features from segmented tissues.
  • Implemented a genetic algorithm for feature optimization to improve detection accuracy.

Main Results:

  • The developed system demonstrated effective segmentation and detection of brain tumors in MRI scans.
  • Performance was evaluated using metrics including accuracy, sensitivity, specificity, Dice coefficient, Jaccard's coefficient, spatial overlap, AVME, and FoM.
  • The combination of BWT, GLCM, and genetic algorithms showed promise in improving tumor detection capabilities.

Conclusions:

  • The proposed system offers a robust approach to brain tumor detection and classification from MRI data.
  • This method can potentially alleviate the burden on radiologists by providing an accurate and efficient diagnostic tool.
  • Further research and validation are warranted to integrate this system into clinical practice for improved patient outcomes.