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Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine

Şerife Gengeç Benli1, Merve Andaç1

  • 1Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey.

Diagnostics (Basel, Switzerland)
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Summary

Magnetic resonance imaging reveals distinct textural characteristics in specific brain regions, aiding in schizophrenia diagnosis. The left hemisphere

Keywords:
machine learningschizophreniastructural MR images

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Area of Science:

  • Neuroimaging
  • Psychiatric Disorders
  • Biomarker Discovery

Background:

  • Schizophrenia diagnosis is complex and crucial for effective treatment.
  • Magnetic resonance (MR) imaging offers potential biomarkers for schizophrenia.
  • Analyzing textural characteristics in specific brain regions may differentiate individuals with schizophrenia from healthy controls.

Purpose of the Study:

  • To numerically analyze textural differences in bilateral amygdala, caudate, pallidum, putamen, and thalamus regions between schizophrenia patients and healthy controls using structural MR images.
  • To identify which brain hemisphere exhibits more distinctive textural features.
  • To compare the classification performance of various machine learning methods for schizophrenia detection.

Main Methods:

  • Gray Level Co-occurrence Matrix (GLCM) features were extracted from five specific brain regions (amygdala, caudate, pallidum, putamen, thalamus) in both hemispheres.
  • Machine learning algorithms including Adaboost, Gradient Boost, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Naive Bayes were employed for classification.
  • Classification success was evaluated based on accuracy, sensitivity, specificity, and Area Under the Curve (AUC).

Main Results:

  • Textural features from the five specified brain regions, particularly in the left hemisphere, showed higher classification performance for schizophrenia compared to healthy individuals.
  • The Linear Discriminant Analysis (LDA) algorithm achieved superior classification results.
  • LDA demonstrated 100% AUC, 94.4% accuracy, 92.31% sensitivity, 100% specificity, and a 91.9% F1 score.

Conclusions:

  • Textural characteristics of specific brain regions, rather than the entire brain, are significant indicators for identifying schizophrenia.
  • The left hemisphere's textural features show particular promise as biomarkers for schizophrenia detection.
  • Machine learning, especially LDA, can effectively utilize these textural features for accurate schizophrenia diagnosis.