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Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image

Iran Sarafraz1, Hamed Agahi1, Azar Mahmoodzadeh1

  • 1Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

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Summary

This study introduces an adaptive method for configuring convolutional neural networks (CNNs) for neonatal brain image segmentation. The approach optimizes CNNs using reinforcement learning, improving segmentation accuracy for infant MRI scans.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Convolutional Neural Networks (CNNs) are effective for brain image segmentation but require specific adaptations for neonatal brain images due to developmental differences.
  • Optimal CNN architecture and parameters are crucial for accurate neonatal brain image segmentation.

Purpose of the Study:

  • To present an adaptive method for automatic CNN configuration tailored for neonatal brain image segmentation.
  • To optimize CNN hyperparameters, including filter dimensions and pooling functions, using reinforcement learning and a Look-Ahead (LA) model.

Main Methods:

  • An encoder-decoder CNN structure was employed.
  • Reinforcement learning and an LA model were utilized to determine optimal hyperparameters (filter size, length, width, pooling type).
  • Segmentation quality was evaluated using DICE and Average Symmetric Surface Distance (ASD) metrics.

Main Results:

  • The proposed adaptive method successfully configured CNNs for neonatal brain image segmentation.
  • The optimized CNNs achieved higher quality and accuracy in segmenting neonatal brain images (NBI) compared to previous methods.
  • Evaluation on an infant MRI database demonstrated the effectiveness of the approach.

Conclusions:

  • The adaptive CNN configuration method significantly enhances the accuracy and quality of neonatal brain image segmentation.
  • This approach provides a robust framework for optimizing deep learning models in specialized medical imaging applications.
  • The findings suggest a promising direction for improving automated analysis of infant brain development through advanced AI techniques.