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A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for

Hisham Allahem1, Sameh Abd El-Ghany1, A A Abd El-Aziz1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|June 13, 2025
PubMed
Summary

This study introduces ViT-PCA-RF, a novel hybrid computer-aided diagnosis system for accurate brain tumor classification using MRI scans. The model achieves high performance, enabling earlier detection and improved patient outcomes.

Keywords:
MRIPCARFViTbrain tumor MRI datasetbrain tumorscancerdeep learningmachine learning

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Area of Science:

  • Neuroscience and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Brain tumors are life-threatening conditions requiring accurate and timely diagnosis.
  • Manual analysis of Magnetic Resonance Imaging (MRI) for brain tumors is challenging and prone to errors.
  • Early detection and classification are critical for effective treatment and improved patient prognosis.

Purpose of the Study:

  • To develop and evaluate a novel hybrid computer-aided diagnosis (CAD) system for brain tumor classification.
  • To enhance the accuracy and efficiency of brain tumor detection using Magnetic Resonance Imaging (MRI).
  • To leverage advanced machine learning and deep learning techniques for improved diagnostic outcomes.

Main Methods:

  • A hybrid CAD approach, ViT-PCA-RF, integrating Vision Transformer (ViT), Principal Component Analysis (PCA), and Random Forest (RF) was developed.
  • ViT was utilized for feature extraction, PCA for dimensionality reduction, and RF for multi-class brain tumor classification.
  • The model was trained and validated on the Brain Tumor MRI (BTM) dataset, with preprocessing including resizing and normalization.

Main Results:

  • The ViT-PCA-RF model demonstrated exceptional performance in brain tumor classification.
  • Achieved high accuracy (99%), specificity (99.4%), precision (98.1%), recall (98.1%), and F1 score (98.1%).
  • Outperformed traditional classifiers in identifying and classifying brain tumors from MRI scans.

Conclusions:

  • The developed ViT-PCA-RF model shows significant potential for precise and effective brain tumor detection.
  • The hybrid approach combining ViT, PCA, and RF offers a promising advancement in AI-driven medical diagnostics.
  • This system can aid in early detection, timely intervention, and improved patient management for brain tumors.