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Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images.

Ahmad Alaiad1, Aya Migdady1, Ra'ed M Al-Khatib2

  • 1Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan.

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Summary

This study demonstrates AutoKeras, an automated deep learning framework, effectively detects malaria parasites in blood smear images. The approach achieved 95.6% accuracy, outperforming traditional neural networks without prior deep learning knowledge.

Keywords:
artificial intelligence (AI)convolutional neural network (CNN)deep learning (DL)malaria parasites

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Pathology
  • Parasitology

Background:

  • Automated deep learning (AI) shows promise but has limited clinical applications.
  • Detecting malaria parasites in blood smears is crucial for diagnosis.

Purpose of the Study:

  • To evaluate the efficacy of the open-source AutoKeras framework for malaria detection in blood smear images.
  • To assess AutoKeras's ability to identify optimal neural networks for classification without prior deep learning expertise.

Main Methods:

  • Utilized the AutoKeras automated deep learning framework.
  • Trained and evaluated the model on a dataset of 27,558 blood smear images.
  • Compared performance against traditional deep neural network methods.

Main Results:

  • The AutoKeras model achieved a high accuracy of 95.6% in detecting malaria parasites.
  • Demonstrated superior performance compared to conventional neural network approaches.
  • The framework's robustness stems from its ability to learn optimal network architectures autonomously.

Conclusions:

  • AutoKeras offers a robust and efficient solution for malaria parasite detection in blood smears.
  • Automated deep learning frameworks can significantly advance clinical diagnostics with minimal prior expertise.
  • This approach surpasses traditional methods in accuracy and efficiency for malaria detection.