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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Related Experiment Video

Updated: Oct 23, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Neural Style Transfer as Data Augmentation for Improving COVID-19 Diagnosis Classification.

Netzahualcoyotl Hernandez-Cruz1, David Cato2, Jesus Favela3

  • 1Ulster University, Belfast, UK.

SN Computer Science
|August 18, 2021
PubMed
Summary

This study uses cycle-generative adversarial networks to create more COVID-19 positive chest X-ray images from existing ones. This data augmentation significantly improves the performance of AI models diagnosing COVID-19 from X-rays.

Keywords:
Convolutional neural networkData augmentationGenerative adversarial networkNeural style transferTransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Coronavirus disease 2019 (COVID-19) has caused millions of deaths and strained healthcare systems.
  • Accurate and accessible diagnostic tools are crucial for managing the pandemic, but current COVID-19 testing is insufficient and costly.
  • Chest X-rays are vital for diagnosing respiratory illnesses, including COVID-19, but limited availability of positive COVID-19 X-ray datasets hinders AI model development.

Purpose of the Study:

  • To address the scarcity of COVID-19 positive chest X-ray images for training diagnostic AI models.
  • To evaluate the effectiveness of cycle-generative adversarial networks (CycleGAN) for augmenting limited COVID-19 positive X-ray datasets.
  • To enhance the performance of convolutional neural networks (CNNs) in classifying COVID-19 from chest X-rays through data augmentation.

Main Methods:

  • Utilized cycle-generative adversarial networks (CycleGAN), a technique from neural style transfer, to synthesize realistic COVID-19 positive X-ray images from COVID-19 negative images.
  • Augmented a dataset of chest X-rays by generating synthetic COVID-19 positive samples.
  • Integrated the augmented dataset with standard transfer learning techniques to train various convolutional neural networks (CNNs).

Main Results:

  • Demonstrated a significant increase in the mean macro F1-score by over 21% for COVID-19 classification.
  • Achieved statistical significance with a one-tailed t-score of 2.68 and a p-value of 0.01, supporting the effectiveness of the augmentation method.
  • Showcased improved performance of common CNN architectures when trained on the augmented dataset.

Conclusions:

  • Cycle-generative adversarial networks are effective for augmenting limited COVID-19 positive chest X-ray datasets.
  • This data augmentation approach, combined with transfer learning, substantially improves the diagnostic performance of AI classifiers for COVID-19 detection.
  • The method offers a viable solution to overcome data scarcity challenges in developing robust AI tools for infectious disease diagnosis.