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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour

Amin Abdollahi Dehkordi1, Mehdi Neshat2,3, Alireza Khosravian4

  • 1Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. amin.abdollahi.dehkordi@gmail.com.

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Summary

This study introduces a fast, efficient deep learning model for medical image classification. The novel approach combines lightweight convolutional neural networks (CNNs) with advanced hyperparameter optimization, achieving superior accuracy in brain tumor detection.

Keywords:
Convolutional neural networks (CNN)Image classificationNonlinear Lévy chaotic moth flame optimiser (NLCMFO)Optimization

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning

Background:

  • Deep convolutional neural networks (CNNs) excel in medical image classification but demand significant computational resources and complex hyperparameter optimization.
  • Optimizing deep CNN hyperparameters is computationally intensive, posing challenges for researchers without high-performance computing access.

Purpose of the Study:

  • To develop a computationally efficient and highly accurate model for medical image classification.
  • To address the limitations of traditional deep CNNs by introducing a lightweight CNN combined with an advanced optimizer for hyperparameter tuning.

Main Methods:

  • Development of lightweight CNNs integrated with the Nonlinear Lévy chaotic moth flame optimiser (NLCMFO) for automatic hyperparameter optimization.
  • NLCMFO enhances exploration and exploitation phases using Lévy flight, chaotic parameters, and nonlinear control mechanisms.
  • Empirical analysis using a dataset of 2314 brain tumor detection images.

Main Results:

  • The proposed CNN_NLCMFO model achieved 92.40% accuracy, outperforming a non-optimized CNN by 5%.
  • It surpassed established models like DarkNet19 (96.41%), EfficientNetB0 (96.32%), Xception (96.41%), ResNet101 (92.15%), and InceptionResNetV2 (95.63%).
  • Performance gains ranged from 1% to 5.25% compared to existing models.

Conclusions:

  • The lightweight CNN combined with NLCMFO offers a computationally efficient and accurate solution for medical image classification.
  • This approach effectively addresses the challenges posed by traditional deep CNNs in terms of computational cost and hyperparameter optimization.
  • The findings highlight the potential of optimized lightweight CNNs for practical applications in medical diagnostics.