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Related Experiment Videos

Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification.

Manoj Kumar1, Noor Mohd1, G Shivam1

  • 1Graphic Era (Deemed to be University), Dehradun, India.

Scientific Reports
|March 10, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel Aquila Optimizer-Harris Hawks Optimization (AO-HHO) framework for tuning convolutional neural networks (CNNs) in brain MRI analysis. The AO-HHO framework significantly improves accuracy and reduces computational cost for medical imaging decision support.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Magnetic resonance imaging (MRI) analysis faces challenges with interclass similarity, data imbalance, and sensitive clinical decisions.
  • Convolutional neural networks (CNNs) performance heavily depends on hyperparameter tuning, which is often computationally expensive.
  • Existing metaheuristic algorithms for CNN hyperparameter optimization may lack balance between exploration and exploitation.

Purpose of the Study:

  • To propose a hybrid optimization framework, Aquila Optimizer-Harris Hawks Optimization (AO-HHO), for robust CNN hyperparameter tuning.
  • To address the limitations of traditional optimization methods in medical imaging analysis.
  • To enhance the accuracy and efficiency of brain tumor classification using MRI data.

Main Methods:

Keywords:
Arithmetic Optimization AlgorithmBrain Tumor ClassificationHarris Hawks OptimizationHybrid Metaheuristic OptimizationMagnetic Resonance Imaging

Related Experiment Videos

  • Developed a hybrid AO-HHO framework integrating Aquila Optimizer's global exploration and Harris Hawks Optimization's local exploitation.
  • Applied the AO-HHO framework to fine-tune critical CNN hyperparameters (learning rate, batch size, filters, dropout, optimizer type).
  • Evaluated the framework on a dataset of 7,023 brain MRI images across glioma, meningioma, pituitary tumor, and non-tumor categories.

Main Results:

  • The AO-HHO-tuned CNN achieved significantly higher accuracy, precision, recall, and F1-score compared to conventional algorithms (PSO, GA, WOA), with performance metrics ranging from 78-83%.
  • The proposed AO-HHO framework demonstrated superior computational efficiency, reducing training time to 77.85 seconds compared to over 300 seconds for baseline optimizers.
  • The framework effectively handled challenges like interclass similarity and data imbalance in brain MRI classification.

Conclusions:

  • The AO-HHO framework offers a reliable, accurate, and computationally efficient solution for CNN hyperparameter optimization in medical imaging.
  • This approach is suitable for real-time medical imaging decision-support applications with limited computational resources.
  • The study highlights the potential of hybrid metaheuristic algorithms for advancing AI in healthcare.