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Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Abdullah A Asiri1, Ahmad Shaf2, Tariq Ali2

  • 1Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Najran, Saudi Arabia.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary

Optimizing convolutional neural network (CNN) hyperparameters significantly improves brain tumor diagnostic accuracy. This refined CNN model enhances precision, recall, and F1-scores for glioma, meningioma, and pituitary tumor detection.

Keywords:
Brain tumor diagnosisDecision-making processesFeature extractionHyperparameter tuningModel complexityOptimization techniquesSpatial resolution

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Convolutional Neural Networks (CNNs) are crucial for medical image analysis.
  • Hyperparameter tuning is essential for optimizing CNN performance in diagnostics.
  • Brain tumor diagnosis accuracy relies heavily on effective CNN model configuration.

Purpose of the Study:

  • To develop and validate a refined CNN hyperparameter model for brain tumor diagnosis.
  • To optimize critical CNN parameters including filters, stride, pooling, activation functions, learning rate, batch size, and layers.
  • To enhance the accuracy and reliability of automated brain tumor detection using MRI data.

Main Methods:

  • Utilized two public brain tumor MRI datasets (7,023 and 253 images).
  • Systematically optimized CNN hyperparameters: filter number/size, stride, padding, pooling, activation functions, learning rate, batch size, and number of layers.
  • Evaluated model performance using precision, recall, F1-score, and accuracy metrics.

Main Results:

  • Achieved 96% accuracy and average 94.25% precision, recall, and F1-score on Dataset 1 (4 classes).
  • Attained 88% accuracy and average 87.5% precision, recall, and F1-score on Dataset 2 (2 classes).
  • Demonstrated superior performance compared to existing techniques through comprehensive comparison.

Conclusions:

  • The optimized CNN hyperparameter model significantly enhances brain tumor diagnostic performance.
  • Systematic hyperparameter fine-tuning improves model accuracy and generalization capabilities.
  • This tool offers medical experts a more precise and efficient aid for brain tumor diagnosis.