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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Covid-19 Imaging Tools: How Big Data is Big?

K C Santosh1, Sourodip Ghosh2

  • 1KC's PAMI Ressarch Lab - Computer Science, University of South Dakota, Vermillion, SD, 57069, USA. santosh.kc@ieee.org.

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AI tools for medical imaging, including chest CT and X-ray, show limitations in COVID-19 screening. Dataset size and data augmentation do not improve performance for detecting all COVID-19 cases.

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Big dataChest Computed TomographyChest X-rayCovid-19Medical imaging tools

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic highlighted the need for rapid and accurate diagnostic tools.
  • Medical imaging, particularly chest Computed Tomography (CT) and X-ray, plays a crucial role in diagnosing pneumonia and related conditions.
  • Artificial intelligence (AI) has emerged as a powerful tool for analyzing medical images.

Purpose of the Study:

  • To analyze the performance of AI-driven medical imaging tools for COVID-19 detection in 2020.
  • To evaluate the impact of dataset size and complexity on AI model performance.
  • To assess the effectiveness of transfer learning and data augmentation in developing COVID-19 diagnostic models.

Main Methods:

  • Analysis of AI-driven tools using chest CT and X-ray data.
  • Evaluation based on dataset size, model fitting (overfitting/underfitting), transfer learning, and data augmentation.
  • Consideration of the limitations of current medical imaging tools in analyzing model fitting.

Main Results:

  • AI tools do not explicitly analyze model fitting criteria.
  • Transfer learning can aid in building COVID-19 models with limited data, but primarily for educational purposes.
  • Neither dataset size nor data augmentation significantly improved COVID-19 screening accuracy due to the diverse manifestations of the disease and the inability of augmentation to create novel cases.

Conclusions:

  • Current AI-driven medical imaging tools face challenges in effectively screening for COVID-19.
  • Larger datasets and data augmentation do not guarantee improved detection of all COVID-19 variations.
  • Further research is needed to enhance the reliability and generalizability of AI models for infectious disease diagnostics.