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Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority

Muhammad A Khan1, Ikram U Lali2, Amjad Rehman3

  • 1Department of Computer Science and Engineering, HITEC University Museum Road, Taxila, Pakistan.

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|February 26, 2019
PubMed
Summary
This summary is machine-generated.

This study presents an automated system for brain tumor identification using magnetic resonance imaging (MRI). The novel approach enhances tumor detection and classification accuracy, aiding in cancer diagnosis and treatment.

Keywords:
classificationfeatures extractionpreprocessingreductionsegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Brain tumor identification from MRI is crucial for diagnosis and treatment.
  • Computerized techniques can aid clinicians in managing brain cancer.

Purpose of the Study:

  • To develop an automated system for brain tumor extraction and classification from MRI scans.
  • To improve the precision and accuracy of brain tumor detection.

Main Methods:

  • The system employs a five-step process: contrast enhancement, segmentation, feature extraction, feature selection, and classification.
  • Key techniques include gamma contrast stretching, marker-based watershed segmentation, chi-square feature selection, and support vector machine classification.

Main Results:

  • The proposed system demonstrated superior performance compared to existing methods.
  • High-ranked features (70%) were selected using a chi-square approach, followed by serial feature concatenation.

Conclusions:

  • The developed automated system effectively extracts and classifies brain tumors from MRI data.
  • The system offers enhanced precision and accuracy, outperforming current methodologies in brain tumor identification.