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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A transfer learning based deep learning model to diagnose covid-19 CT scan images.

Sanat Kumar Pandey1, Ashish Kumar Bhandari1, Himanshu Singh2

  • 1Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, India.

Health and Technology
|June 14, 2022
PubMed
Summary

A novel deep learning method accurately diagnoses coronavirus disease-2019 (COVID-19) using segmented CT scans. This approach enhances diagnostic speed and accuracy, crucial for managing pandemic patient loads.

Keywords:
Arterial blood gas analysisComplete blood countImage segmentationOtsu’sThresholding

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The COVID-19 pandemic necessitates efficient diagnostic tools, especially when resources are strained.
  • Accurate and rapid patient diagnosis is critical for effective treatment and resource allocation.

Purpose of the Study:

  • To develop and validate an automated deep learning-based method for diagnosing COVID-19 from medical images.
  • To compare the efficacy of different image segmentation techniques and deep learning models for COVID-19 detection.

Main Methods:

  • CT scan images were pre-processed and segmented using various algorithms, including Otsu's method.
  • Deep learning models (ResNet-50, MobileNet, VGG-16) were employed for image classification.
  • Performance was evaluated based on accuracy, sensitivity, precision, and specificity.

Main Results:

  • Otsu's algorithm demonstrated superior performance in image segmentation compared to other methods.
  • The VGG-16 model, combined with Otsu's segmented CT images, achieved a high diagnostic accuracy of 99.28%.
  • The proposed method significantly outperformed non-segmented image analysis.

Conclusions:

  • Deep learning, particularly the VGG-16 model with Otsu's segmentation, offers a highly accurate and efficient solution for COVID-19 diagnosis.
  • This automated approach can reduce diagnostic complexity and time, aiding in critical pandemic situations.
  • Integration with Arterial Blood Gas (ABG) analysis allows for severity assessment and tailored treatment protocols.