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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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 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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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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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:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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 VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

Updated: Oct 29, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation.

Hemalatha Munusamy1, J M Karthikeyan1, G Shriram1

  • 1Department of Information Technology, Anna University, MIT Campus, Chennai, India.

Biocybernetics and Biomedical Engineering
|July 14, 2021
PubMed
Summary

A new deep learning model, FractalCovNet, accurately detects COVID-19 from chest scans. This AI tool aids in rapid diagnosis and lesion identification, crucial for controlling the SARS-II-COV pandemic.

Keywords:
COVID-19CT-scan image segmentationChest X-ray classificationFractalCovNetU-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • Accurate and rapid diagnosis of COVID-19 is critical for effective treatment and containment.
  • Chest X-rays and CT scans are vital diagnostic tools for identifying COVID-19.
  • Automated analysis of medical images can significantly aid in pandemic response.

Purpose of the Study:

  • To develop a novel deep learning architecture, FractalCovNet, for the automated detection and segmentation of COVID-19 lesions.
  • To evaluate the performance of FractalCovNet for both image segmentation (CT scans) and classification (X-rays).
  • To compare FractalCovNet against existing state-of-the-art models for COVID-19 diagnosis.

Main Methods:

  • Developed FractalCovNet, integrating fractal blocks and U-Net for CT scan lesion segmentation.
  • Utilized FractalCovNet with transfer learning for COVID-19 classification from X-ray images.
  • Benchmarked segmentation performance against U-Net, DenseUNet, Segnet, ResnetUNet, and FCN.
  • Benchmarked classification performance against ResNet50, Xception, InceptionResNetV2, VGG-16, and DenseNet.

Main Results:

  • FractalCovNet achieved high F-measure and precision values in predicting COVID-19 lesions.
  • The model demonstrated superior performance compared to other state-of-the-art methods in segmentation tasks.
  • The proposed architecture effectively identifies lesion regions in chest CT scans without manual annotation.
  • Achieved high accuracy in classifying COVID-19 from X-ray images.

Conclusions:

  • FractalCovNet offers a highly accurate and efficient deep learning solution for COVID-19 diagnosis.
  • The model facilitates rapid identification of COVID-19 patients and lesion localization.
  • This approach aids in controlling the outbreak of SARS-II-COV by enabling faster clinical decisions.