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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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Related Experiment Video

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer

R Betshrine Rachel1, H Khanna Nehemiah1, Vaibhav Kumar Singh2

  • 1Ramanujan Computing Centre, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India.

Journal of X-Ray Science and Technology
|January 8, 2024
PubMed
Summary
This summary is machine-generated.

A new Computer Aided Diagnosis (CAD) system effectively identifies Covid-19 from chest CT scans. Feature selection using Whale Optimization Algorithm (WOA) significantly improved Multi-Layer Perceptron (MLP) accuracy to 88.94%.

Keywords:
Covid-19MLPSVMWOAkendall’s correlation coefficient graph

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Diagnostic Systems

Background:

  • Coronavirus disease 2019 (Covid-19) is a severe respiratory illness caused by SARS-CoV-2.
  • Accurate and timely diagnosis is crucial for managing Covid-19.
  • Chest Computed Tomography (CT) is a key imaging modality for Covid-19 detection.

Purpose of the Study:

  • To develop and evaluate a Computer Aided Diagnosis (CAD) system for Covid-19 detection using chest CT slices.
  • To enhance diagnostic accuracy through optimized feature selection.
  • To compare the proposed system's performance against established machine learning classifiers.

Main Methods:

  • Lung tissue segmentation using Otsu's thresholding method.
  • Identification and annotation of Covid-19 lesions as Regions of Interest (ROIs).
  • Feature extraction (texture and shape), selection via Whale Optimization Algorithm (WOA) and Support Vector Machine (SVM) accuracy, and classification using Multi-Layer Perceptron (MLP).

Main Results:

  • The proposed CAD system achieved 88.94% accuracy with feature selection.
  • The MLP classifier without feature selection yielded 80.40% accuracy.
  • The system significantly outperformed eight benchmark Machine Learning classifiers on a real-time dataset.

Conclusions:

  • Feature selection using WOA substantially improves the diagnostic accuracy of the MLP classifier for Covid-19 detection.
  • The developed CAD system demonstrates high efficacy and potential for clinical application in diagnosing Covid-19 from chest CT scans.
  • Statistical analyses confirm the significant impact and superiority of the proposed method on the considered dataset.