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A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.

Zahid Ullah1, Mona Jamjoom2, Manikandan Thirumalaisamy3

  • 1Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.

Biomedical Engineering and Computational Biology
|September 6, 2024
PubMed
Summary
This summary is machine-generated.

This study developed deep learning models, including VGG models, for brain tumor detection using medical images. The VGG models achieved 99% accuracy, demonstrating their effectiveness as a reliable decision support system for clinicians.

Keywords:
Brain tumorCADCNNdetectionimagestransfer learning

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Neuroscience

Background:

  • Brain tumors (BT) are a significant cause of mortality, necessitating advanced diagnostic tools.
  • Computer-aided diagnostic systems (CAD), particularly deep learning, are revolutionizing medical image analysis.
  • Convolution Neural Networks (CNNs) excel at extracting features from medical images for disease detection.

Purpose of the Study:

  • To develop and evaluate deep learning models for the automatic detection of brain tumors from medical images.
  • To compare the performance of CNNs developed from scratch against transfer learning models (VGG-16, VGG-19, LeNet-5).
  • To establish an intelligent decision support system for brain tumor diagnosis.

Main Methods:

  • Utilized a deep learning approach to extract features from brain images for BT detection.
  • Developed and tested CNN models, including VGG-16, VGG-19, and LeNet-5, for brain tumor classification.
  • Employed data augmentation to increase dataset size and hyperparameter tuning for model optimization.

Main Results:

  • VGG models demonstrated superior performance, achieving 99.24% accuracy, 99% precision, 99% recall, 99% specificity, and 99% F1-score.
  • The proposed models outperformed existing state-of-the-art methods in accuracy, sensitivity, specificity, and F1-score.
  • Comparative analysis confirmed the reliability and effectiveness of the developed models.

Conclusions:

  • Deep learning models, especially VGG variants, show high efficacy in detecting brain tumors from medical images.
  • The developed system serves as a reliable tool to aid medical practitioners in diagnosing brain tumors.
  • This research highlights the potential of advanced AI in improving diagnostic accuracy and patient outcomes.