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Brain tumor classification using modified local binary patterns (LBP) feature extraction methods.

Kaplan Kaplan1, Yılmaz Kaya2, Melih Kuncan3

  • 1Kocaeli University, Mechatronics Engineering, 41380, Turkey.

Medical Hypotheses
|April 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces novel nLBP and αLBP feature extraction methods for brain tumor classification. The nLBP method with a d=1 parameter achieved the highest accuracy of 95.56% using the K-Nearest Neighbor (KNN) model.

Keywords:
Brain tumor classificationLBPMachine learning techniquesNLBP and αLBP

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Machine Learning

Background:

  • Accurate brain tumor classification is crucial for effective treatment and patient survival.
  • Manual diagnosis from MR images relies heavily on radiologist expertise, introducing variability.
  • Automated classification models aim to reduce human error and standardize diagnosis.

Purpose of the Study:

  • To evaluate the efficacy of novel nLBP and αLBP feature extraction techniques for classifying common brain tumors (Glioma, Meningioma, Pituitary).
  • To compare the performance of these methods against classical LBP using various machine learning classifiers.
  • To identify the optimal feature extraction and classification model for high-accuracy brain tumor diagnosis.

Main Methods:

  • Utilized two distinct feature extraction methods: neighborhood LBP (nLBP) with a distance parameter 'd', and angular LBP (αLBP) with specified angles.
  • Applied these methods to a dataset of brain tumor MR images from Nanfang Hospital and Tianjin Medical University General Hospital.
  • Employed K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Random Forest (RF), A1DE, and Linear Discriminant Analysis (LDA) for classification.

Main Results:

  • The nLBP feature extraction method, particularly with d=1, demonstrated superior performance.
  • The combination of nLBP (d=1) and the KNN classifier yielded the highest classification accuracy of 95.56%.
  • Both nLBP and αLBP showed promise in capturing relevant features for tumor classification compared to classical LBP.

Conclusions:

  • The nLBP feature extraction method, coupled with the KNN classifier, represents a highly effective approach for automated brain tumor classification.
  • This automated method has the potential to significantly improve diagnostic accuracy and efficiency in clinical practice.
  • Further research can explore variations of LBP and other advanced machine learning models for enhanced brain tumor detection.