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Background <p>Brain tumors are life-threatening neurological conditions that develop due to uncontrolled cell growth in the brain. The survival rate of this illness is gradually decreasing due to the lack of early and accurate diagnosis of brain tumors. Multiple classification methods have been developed for the classification of brain tumors, but they have resulted in several limitations, such as complications in segmentation, inconsistency in tumor features, data inequity, low performance, and high error.</p> Methods <p>To overcome the limitations, this research proposed a Producer Scrounger Foraging Optimized Gated Recurrent Unit-Deep Bidirectional Long Short-Term Memory (PSF-GRBM) model for effective brain tumor classification. Additionally, the integration of PSF optimization in the proposed model reduces the high-complexity problems in various dimensions and achieves effective results.</p> Results <p>The experimental analysis reveals that the proposed method attained 95.74% of accuracy, 95.67% of sensitivity, and 95.81% of specificity, using the MSD dataset, which highlights the improved performance of the PSF-GRBM model in brain tumor classification and grading.</p> Conclusion <p>The PSF-GRBM model achieves effective classification through an incentive learning mechanism, categorizing brain tumors into four grades: grade 0 for a normal brain, grade 1 for non-enhancing and necrotic tumor core, grade 2 for peritumoral edema, and grade 3 for Gadolinium (GD) enhancing tumors.</p>

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

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Vikrant Chole,
  • Jhankar Moolchandani,
  • Sachin Verma,
  • Shilpa Satone,
  • Minal Thawakar

摘要

Background

Brain tumors are life-threatening neurological conditions that develop due to uncontrolled cell growth in the brain. The survival rate of this illness is gradually decreasing due to the lack of early and accurate diagnosis of brain tumors. Multiple classification methods have been developed for the classification of brain tumors, but they have resulted in several limitations, such as complications in segmentation, inconsistency in tumor features, data inequity, low performance, and high error.

Methods

To overcome the limitations, this research proposed a Producer Scrounger Foraging Optimized Gated Recurrent Unit-Deep Bidirectional Long Short-Term Memory (PSF-GRBM) model for effective brain tumor classification. Additionally, the integration of PSF optimization in the proposed model reduces the high-complexity problems in various dimensions and achieves effective results.

Results

The experimental analysis reveals that the proposed method attained 95.74% of accuracy, 95.67% of sensitivity, and 95.81% of specificity, using the MSD dataset, which highlights the improved performance of the PSF-GRBM model in brain tumor classification and grading.

Conclusion

The PSF-GRBM model achieves effective classification through an incentive learning mechanism, categorizing brain tumors into four grades: grade 0 for a normal brain, grade 1 for non-enhancing and necrotic tumor core, grade 2 for peritumoral edema, and grade 3 for Gadolinium (GD) enhancing tumors.