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A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

Hareem Kibriya1, Rashid Amin2, Jinsul Kim3

  • 1Department of Computer Sciences, University of Engineering and Technology, Taxila 47050, Pakistan.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI method for accurate brain tumor detection using an ensemble of deep and hand-crafted features. The approach achieved 99% accuracy, offering a reliable tool for radiologists using MRI scans.

Keywords:
GLCMKNNVGG16artificial intelligencebrain tumor

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors are a severe cancer type requiring fast and accurate detection.
  • Existing automated AI methods for tumor diagnosis often show poor performance.
  • There is a critical need for efficient and precise brain tumor diagnostic techniques.

Purpose of the Study:

  • To propose a novel, efficient, and accurate method for brain tumor detection.
  • To develop an ensemble feature vector (FV) combining deep and hand-crafted features.
  • To enhance the discriminating capabilities of diagnostic models for brain tumors.

Main Methods:

  • An ensemble feature vector (FV) was created by combining Gray-Level Co-occurrence Matrix (GLCM) features and VGG16 deep features.
  • The novel FV was classified using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) algorithms.
  • The proposed methodology was validated using cross-tabulated data for performance assessment.

Main Results:

  • The ensemble FV demonstrated superior discriminating capabilities compared to independent feature vectors.
  • The proposed framework achieved a maximum accuracy of 99% when using the ensemble FV.
  • The method showed robustness and efficacy in detecting brain tumors from MRI images.

Conclusions:

  • The proposed novel approach for brain tumor detection is reliable and effective.
  • The methodology can be deployed in real-world environments for accurate tumor diagnosis via MRI.
  • Radiologists can utilize this technique to improve patient health outcomes through early and precise detection.