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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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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Imaging Studies for Cardiovascular System IV: CMRI01:21

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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models.

Saleh Almuayqil1, Sameh Abd El-Ghany1,2, Abdulaziz Shehab1,2

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

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This study developed deep learning models for rapid COVID-19 diagnosis using CT scans and X-rays. The models achieved superior performance, addressing clinician shortages and improving diagnostic speed.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic highlighted the need for rapid diagnostic tools.
  • Clinician shortages necessitate automated solutions for patient care.
  • Timely diagnosis is crucial for effective COVID-19 management and containment.

Purpose of the Study:

  • To develop and evaluate deep learning models for accurate COVID-19 detection.
  • To utilize chest CT scans and X-ray images for automated diagnosis.
  • To address diagnostic latency challenges during the pandemic.

Main Methods:

  • Five deep learning models (EfficientB0, VGG-19, DenseNet121, EfficientB7, MobileNetV2) were fine-tuned.
  • Models were trained to classify CT scan and chest X-ray images as COVID-19 positive or negative.
  • Performance was evaluated using precision, sensitivity, specificity, F1 score, accuracy, and data access time.

Main Results:

  • The proposed deep learning approach demonstrated superior diagnostic performance.
  • Achieved high precision, sensitivity, specificity, and F1 scores.
  • Outperformed existing state-of-the-art methods in COVID-19 detection accuracy and speed.

Conclusions:

  • Deep learning models offer a promising solution for rapid and accurate COVID-19 diagnosis.
  • Automated image analysis can alleviate pressure on healthcare systems during pandemics.
  • The study provides a validated method for enhancing diagnostic capabilities.