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Related Experiment Video

Updated: Aug 15, 2025

Brain Morphology of Cannabis Users With or Without Psychosis: A Pilot MRI Study
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Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features.

K Suresh Manic1, Venkatesan Rajinikanth2, Ali Saud Al-Bimani1

  • 1National University of Science and Technology, Muscat P.O. Box 112, Oman.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for automatically detecting schizophrenia (SCZ) from brain MRI scans. Combining deep and handcrafted features achieved over 95% accuracy, outperforming other methods for reliable disease screening.

Keywords:
Markov random fieldVGG19brain MRIdisease detectionlocal binary patternschizophrenia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Neuroscience

Background:

  • Brain abnormalities necessitate accurate diagnostic methods.
  • Bio-image analysis offers higher accuracy than bio-signal (EEG) analysis for brain condition screening.
  • Early and reliable identification of schizophrenia (SCZ) is crucial for patient management.

Purpose of the Study:

  • To develop a robust framework for automatic schizophrenia detection using brain MRI slices.
  • To enhance diagnostic accuracy by integrating deep and handcrafted features.
  • To validate the proposed framework's clinical significance.

Main Methods:

  • Utilized VGG16 for deep feature (DF) extraction from MRI slices.
  • Collected handcrafted features (HF) and employed the mayfly algorithm for optimal feature selection.
  • Concatenated DF and HF, followed by binary classification for SCZ identification.

Main Results:

  • The combined DF+HF approach achieved a superior accuracy of >95% for schizophrenia screening.
  • Deep features (DF) alone yielded >91% accuracy, while handcrafted features (HF) achieved >85%.
  • The proposed framework demonstrated high performance in classifying SCZ from MRI data.

Conclusions:

  • The developed framework provides a reliable and accurate method for automated schizophrenia screening from brain MRI.
  • Integrating deep and handcrafted features significantly improves diagnostic performance.
  • This approach holds clinical significance for future patient screening and diagnosis.