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A Fair Performance Comparison between Complex-Valued and Real-Valued Neural Networks for Disease Detection.

Mario Jojoa1, Begonya Garcia-Zapirain2, Winston Percybrooks1

  • 1Department of Electrical and Electronics Engineering, University of North, Barranquilla 080002, Colombia.

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Complex-valued neural networks outperform real-valued ones for detecting anomalies in biosignals like melanoma and heart murmurs. This novel approach enhances early disease detection systems.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Anomalous patterns in biosignals pose challenges for early disease detection.
  • Developing portable systems for real-time analysis requires robust classification methods.

Purpose of the Study:

  • To compare the efficacy of complex-valued versus real-valued convolutional neural networks for biosignal anomaly classification.
  • To evaluate the potential of complex-valued networks for early disease detection of melanoma and heart murmurs.

Main Methods:

  • A comparative study using two similar convolutional neural network architectures, one operating in the complex domain and the other in the real domain.
  • Experiments conducted on three clinical datasets: ISIC2017, PH2, and Pascal.
  • Statistical analysis using mean comparison hypothesis tests to validate findings.

Main Results:

  • Complex-valued networks demonstrated superior performance across Precision, Recall, F1 Score, Accuracy, and Specificity metrics.
  • In melanoma detection, the best complex-valued classifier achieved a Euclidean distance of 0.26127 in ROC space, compared to 0.36022 for the best real-valued classifier.
  • Complex-valued networks showed a 27.46% superiority in discriminating features for anomaly detection.

Conclusions:

  • Complex-valued neural networks offer enhanced feature extraction capabilities for biosignal analysis.
  • The findings suggest complex-valued networks are a powerful approach for developing portable early disease detection systems.
  • This study highlights the potential of complex-valued approaches for improving diagnostic accuracy in medical applications.