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DCNN-FuzzyWOA: Artificial Intelligence Solution for Automatic Detection of COVID-19 Using X-Ray Images.

Abbas Saffari1, Mohammad Khishe1, Mokhtar Mohammadi2

  • 1Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran.

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Summary
This summary is machine-generated.

This study introduces FuzzyWOA, a novel whale optimization algorithm enhanced by a fuzzy system, for training deep convolution neural networks (DCNNs) to detect COVID-19 from X-ray images. The FuzzyWOA model achieved 100% accuracy and F1-Score, outperforming other methods.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Deep Convolution Neural Networks (DCNNs) are utilized for COVID-19 detection from X-ray images.
  • Gradient Descent-based (GDB) algorithms are commonly used for DCNN training but require manual parameter tuning and are difficult to parallelize on GPUs.
  • Existing training methods present challenges in efficiency and optimization for complex DCNN architectures.

Purpose of the Study:

  • To propose an optimized training algorithm for DCNNs applied to COVID-19 detection.
  • To introduce the FuzzyWOA algorithm, integrating a fuzzy system with the Whale Optimization Algorithm (WOA) for enhanced DCNN training.
  • To evaluate the performance of the proposed DCNN-FuzzyWOA model against benchmark models using the COVID-Xray-5k dataset.

Main Methods:

  • Development of the FuzzyWOA algorithm by integrating a fuzzy system to dynamically tune WOA parameters, balancing exploration and exploitation phases.
  • Training a DCNN model using the proposed FuzzyWOA algorithm on the COVID-Xray-5k dataset.
  • Comparative analysis of the DCNN-FuzzyWOA model against DCNN-PSO, DCNN-GA, and LeNet-5 using metrics like accuracy, processing time, STD, ROC, precision-recall, and F1-Score.

Main Results:

  • The DCNN-FuzzyWOA model achieved 100% accuracy and 100% F1-Score within 880.44 seconds over 20 epochs.
  • The proposed FuzzyWOA algorithm demonstrated superior performance compared to Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and LeNet-5.
  • Structural variations of the DCNN (i-6c-2s-12c-2s vs. i-8c-2s-16c-2s) showed performance differences, with the former yielding better results, though both benefited significantly from FuzzyWOA training.

Conclusions:

  • The FuzzyWOA algorithm offers a highly effective and efficient method for training DCNNs for COVID-19 detection from X-ray images.
  • The proposed approach significantly enhances DCNN performance, achieving optimal results in accuracy and classification.
  • FuzzyWOA presents a promising advancement in AI-driven medical diagnostics, particularly for infectious disease detection.