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Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.

S Trisheela1, Roshan Fernandes2, Anisha P Rodrigues3

  • 1Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India.

International Journal of Biomedical Imaging
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI model for early brain tumor detection in MRI scans. The optimized deep learning approach achieved 95% accuracy, improving diagnostic precision for better patient outcomes.

Keywords:
VGGNetbrain tumourdeep learninginvasive weed optimizationmagnetic resonance imagingmodified DarkNet-53

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Malignant brain tumors pose significant health risks, necessitating early and accurate diagnosis.
  • Effective treatment and improved patient outcomes depend on timely detection of brain tumors.
  • Current diagnostic methods can be enhanced by advanced artificial intelligence (AI) techniques.

Purpose of the Study:

  • To improve the accuracy of brain tumor detection in MRI scans using deep learning.
  • To develop an AI model capable of identifying critical features in brain MRI images for early diagnosis.
  • To enhance diagnostic precision for a wide range of brain tumors.

Main Methods:

  • Utilized a modified DarkNet-53 deep learning architecture.
  • Employed invasive weed optimization (IWO) for model optimization.
  • Applied the model to a dataset of 3264 preprocessed MRI scans.

Main Results:

  • Achieved a 95% success rate in brain tumor detection.
  • Demonstrated superior performance compared to existing diagnostic methods.
  • Successfully identified a wide range of brain tumors at an early stage.

Conclusions:

  • The proposed AI-driven method significantly enhances brain tumor detection accuracy in MRI scans.
  • The optimized deep learning model contributes to improved diagnostic precision and patient outcomes.
  • This research highlights the potential of AI in early cancer diagnosis and treatment planning.