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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Imaging Studies I: CT and MRI01:14

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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.
Description of the Procedures
Computed Tomography (CT) scan:
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Computed Tomography01:10

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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Studies III: Computed Tomography01:27

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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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Related Experiment Video

Updated: Sep 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging.

Rajesh Kumar1, Abdullah Aman Khan2, Jay Kumar1

  • 1Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China Huzhou 313001 China.

IEEE Sensors Journal
|July 5, 2022
PubMed
Summary

This study introduces a novel framework for diagnosing COVID-19 using blockchain-based federated learning on CT scans. The method enhances diagnostic accuracy while ensuring data privacy for global collaboration.

Keywords:
COVID-19blockchaindeep learningfederated-learningprivacy-preserved data sharing

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • The rapid spread of COVID-19 necessitates effective diagnostic tools, but current testing kit shortages and data privacy concerns hinder global collaboration.
  • Training deep learning models for COVID-19 detection is challenging due to data heterogeneity from various CT scanners and the need for patient privacy.

Purpose of the Study:

  • To propose a secure and privacy-preserving framework for global deep learning model training using federated learning and blockchain.
  • To develop an effective method for detecting COVID-19 patients from Computed Tomography (CT) images.

Main Methods:

  • A data normalization technique was developed to address heterogeneity from diverse CT scanners.
  • Capsule Network-based segmentation and classification were employed for COVID-19 detection.
  • A collaborative global model was trained using blockchain technology with federated learning to preserve organizational privacy.

Main Results:

  • The proposed framework successfully trained a global deep learning model using federated learning and blockchain.
  • The system demonstrated improved recognition of COVID-19 from CT images by utilizing up-to-date, normalized data.
  • Comprehensive experiments validated the framework's effectiveness, showing superior performance in detecting COVID-19 patients.

Conclusions:

  • The developed framework offers an effective solution for COVID-19 diagnosis through secure, privacy-preserving, and collaborative deep learning.
  • This approach addresses the challenges of data sharing and heterogeneity in medical imaging AI.
  • The study provides a valuable resource for the research community with newly collected COVID-19 patient data.