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Related Experiment Video

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PSF-GRBM: brain tumor classification and grading using optimized gated recurrent unit-deep bidirectional long

Vikrant Chole1, Jhankar Moolchandani2, Sachin Verma3

  • 1Department of Computer Science and Engineering, Amity University Madhya Pradesh, Opposite Airport, Maharajpura, Gwalior, Madhya Pradesh, 474005, India. vikrantchole@gmail.com.

Journal of Neuro-Oncology
|November 10, 2025
PubMed
Summary

This study introduces a novel brain tumor classification model, the Producer Scrounger Foraging Optimized Gated Recurrent Unit-Deep Bidirectional Long Short-Term Memory (PSF-GRBM). The model achieves high accuracy in classifying brain tumors into four grades, improving diagnostic capabilities.

Keywords:
Brain tumorDeep learningIncentive learningProducer scrounger foraging optimizationTumor classification and grading

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Area of Science:

  • Neurology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Brain tumors pose a significant threat to neurological health, with survival rates declining due to diagnostic challenges.
  • Existing brain tumor classification methods face limitations including segmentation issues, feature inconsistency, data imbalance, and low performance.

Purpose of the Study:

  • To develop an advanced model for accurate and effective brain tumor classification.
  • To address the limitations of current methods by proposing a novel optimization and deep learning approach.

Main Methods:

  • Proposed a Producer Scrounger Foraging Optimized Gated Recurrent Unit-Deep Bidirectional Long Short-Term Memory (PSF-GRBM) model.
  • Integrated Producer Scrounger Foraging (PSF) optimization to reduce complexity and enhance performance.

Main Results:

  • The PSF-GRBM model achieved 95.74% accuracy, 95.67% sensitivity, and 95.81% specificity on the MSD dataset.
  • Demonstrated improved performance in brain tumor classification and grading compared to existing methods.

Conclusions:

  • The PSF-GRBM model effectively classifies brain tumors using an incentive learning mechanism.
  • The model categorizes tumors into four grades: normal brain, non-enhancing/necrotic core, peritumoral edema, and enhancing tumors.