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Updated: Sep 18, 2025

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Gradient energy valley optimization enabled segmentation and Spinal VGG-16 Net for brain tumour detection.

Kishore Bhamidipati1, G Anuradha2, Satish Muppidi3

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

Network (Bristol, England)
|June 23, 2025
PubMed
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This summary is machine-generated.

This study introduces Spinal VGG-16-Net for early brain tumour (BT) detection using MRI scans. The novel method achieves high accuracy, aiding in timely diagnosis and treatment of brain tumours.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Anomalous enlargement of brain cells, known as Brain Tumour (BT), poses significant risks due to potential damage to vital neural and vascular structures.
  • Precise and early detection of brain tumours is critical for mitigating severe health consequences and improving patient outcomes.

Purpose of the Study:

  • To introduce an advanced deep learning model, SpinalNet Visual Geometry Group-16 (Spinal VGG-16-Net), for the early and accurate detection of brain tumours.
  • To enhance the performance of brain tumour detection through a combination of image processing techniques and a novel neural network architecture.

Main Methods:

  • Magnetic Resonance Imaging (MRI) data underwent denoising using a bilateral filter.
  • Brain tumour regions were segmented using entropy-based Kapur thresholding, with optimal threshold values determined by Gradient Energy Valley Optimization (GEVO).
Keywords:
Magnetic resonance imagingVisual Geometry Group-16energy valley optimizationstochastic gradient descent

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  • The Spinal VGG-16-Net model, integrating SpinalNet and VGG-16 Net, was employed for feature extraction and final brain tumour detection after image augmentation.

    • Main Results:

    • The proposed Spinal VGG-16-Net model demonstrated a maximum accuracy of 92.14%.
    • High performance metrics were achieved, including a True Positive Rate (TPR) of 93.16%, True Negative Rate (TNR) of 91.35%, Negative Predictive Value (NPV) of 89.73%, and Positive Predictive Value (PPV) of 92.13%.

    Conclusions:

    • The Spinal VGG-16-Net model shows significant promise for early and accurate brain tumour detection.
    • The developed method offers a robust approach for brain tumour diagnosis, outperforming existing schemes in key performance indicators.