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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.
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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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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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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

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OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans.

Oznur Ozaltin1, Ozgur Yeniay1, Abdulhamit Subasi2,3

  • 1Department of Statistics, Institute of Science, Hacettepe University, Ankara, Turkey.

Big Data
|March 17, 2023
PubMed
Summary

A novel deep convolutional neural network (CNN), OzNet, was developed for classifying Coronavirus disease 2019 (COVID-19) computed tomography (CT) scans. Combining OzNet with discrete wavelet transform (DWT) preprocessing achieved over 98.8% classification accuracy, demonstrating high diagnostic potential.

Keywords:
2D-DWTCNNCOVID-19 CT scansclassificationintensity adjustment

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • The global spread of Coronavirus disease 2019 (COVID-19) has significantly increased the demand for rapid diagnostic tools.
  • Computed tomography (CT) scans are crucial for diagnosing COVID-19, but expert interpretation poses a workload challenge.
  • Deep learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis.

Purpose of the Study:

  • To develop and evaluate a novel deep CNN architecture, OzNet, for COVID-19 CT scan classification.
  • To compare the performance of OzNet against established CNN architectures.
  • To assess the impact of different image preprocessing techniques on classification accuracy.

Main Methods:

  • A new deep CNN architecture, OzNet, was designed and implemented.
  • OzNet and several pre-trained CNNs (AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, VGG-16) were trained and tested on COVID-19 CT scan datasets.
  • The classification performance was evaluated using raw CT scans and scans preprocessed with discrete wavelet transform (DWT), intensity adjustment, and gray-to-color conversion.

Main Results:

  • The proposed OzNet architecture, particularly when combined with DWT preprocessing (DWT-OzNet), demonstrated superior performance.
  • The DWT-OzNet model achieved a classification accuracy exceeding 98.8% across all evaluated metrics.
  • The study confirmed that DWT preprocessing generally enhances CNN performance for COVID-19 CT classification compared to raw data.

Conclusions:

  • The DWT-OzNet model represents a highly effective deep learning approach for automated COVID-19 CT scan classification.
  • This method can significantly aid radiologists by reducing workload and potentially improving diagnostic efficiency.
  • The findings highlight the importance of appropriate image preprocessing in optimizing deep learning models for medical imaging applications.