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Improved salp swarm algorithm-driven deep CNN for brain tumor analysis.

Umang Kumar Agrawal1, Nibedan Panda2, Ghanshyam G Tejani3,4

  • 1School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India.

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Summary
This summary is machine-generated.

The Local Search SSA (LS-SSA) improves upon the standard Salp Swarm Algorithm (SSA) by balancing exploration and exploitation. This enhanced metaheuristic algorithm effectively tunes Convolutional Neural Networks (CNNs) for medical imaging tasks.

Keywords:
Brain MRICNNLocal searchMedical imagingPrognosisSSA

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Machine Learning

Background:

  • Swarm-based metaheuristic algorithms, like the Salp Swarm Algorithm (SSA), require a balance between exploration and exploitation for optimal performance.
  • Standard SSA can suffer from premature convergence and getting stuck in local minima due to an imbalance in these operators.
  • This imbalance limits SSA's effectiveness in fine-tuning parameters for complex tasks such as Convolutional Neural Network (CNN) hyperparameter optimization in medical imaging.

Purpose of the Study:

  • To introduce a novel hybrid algorithm, Local Search SSA (LS-SSA), designed to overcome the limitations of the standard SSA.
  • To enhance the exploration and exploitation balance within the SSA framework.
  • To evaluate the efficacy of LS-SSA in improving CNN hyperparameter tuning for medical imaging analysis.

Main Methods:

  • The proposed Local Search SSA (LS-SSA) algorithm is developed by integrating local search mechanisms into the standard SSA.
  • LS-SSA's performance is rigorously assessed using the twenty-eight functions from the IEEE-CEC-2017 benchmark suite.
  • Statistical significance is established through a series of non-parametric tests comparing LS-SSA against contemporary methods.

Main Results:

  • LS-SSA demonstrates superior performance compared to existing methods across various benchmark functions.
  • The algorithm effectively addresses the premature convergence and local minima issues inherent in the standard SSA.
  • Application to CNN hyperparameter tuning on brain MRI datasets shows improved accuracy, reduced standard deviation, lower minimum RMSE, and higher average performance.

Conclusions:

  • LS-SSA provides a more robust and effective approach to optimization problems compared to the standard SSA.
  • The enhanced algorithm achieves faster convergence towards global optima, yielding better candidate solutions.
  • LS-SSA is a highly effective tool for optimizing CNN hyperparameters in medical imaging, particularly for brain MRI analysis, leading to significant improvements in model performance.