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AUTOMATIC OPTIMIZED CNN BASED COVID-19 LUNG INFECTION SEGMENTATION FROM CT IMAGE.

C Priya1, S M H Sithi Shameem Fathima1, N Kirubanandasarathy1

  • 1Department of Electronics and Communication Engineering College, Ramanathapuram, Tamilnadu, India.

Materials Today. Proceedings
|February 10, 2021
PubMed
Summary
This summary is machine-generated.

Automated computed tomography (CT) shows promise for detecting COVID-19 lung infections. However, challenges like image variability and data limitations hinder its effectiveness in clinical settings.

Keywords:
CNNCT imageCT slicesCoronavirusNeural Network

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Area of Science:

  • Medical Imaging
  • Infectious Diseases
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic emerged in early 2020, declared a global health emergency by the WHO.
  • Computed Tomography (CT) presents an opportunity for automated detection of lung infections associated with COVID-19.
  • Traditional diagnostic methods face challenges in rapidly identifying and managing widespread infections.

Purpose of the Study:

  • To explore the potential of automated CT scan analysis for COVID-19 detection.
  • To identify the key challenges and limitations associated with using CT for diagnosing COVID-19 lung infections.
  • To highlight the need for improved methodologies in AI-driven medical image analysis for infectious diseases.

Main Methods:

  • Review of current automated CT detection techniques for lung infections.
  • Analysis of challenges including image segmentation of contaminated areas and low contrast between infected and healthy tissues.
  • Discussion on the limitations of current models in capturing temporal information from CT scans.

Main Results:

  • Automated CT offers a significant potential to enhance COVID-19 diagnosis and management.
  • Significant challenges exist, including variability in infectious properties and difficulty distinguishing subtle infection indicators.
  • Current in-depth models face limitations in data acquisition over time, impacting comprehensive analysis.

Conclusions:

  • Automated CT analysis is a promising avenue for COVID-19 detection, but requires further development.
  • Addressing image variability and data limitations is crucial for improving the accuracy and reliability of CT-based diagnostics.
  • Advancements in AI and imaging techniques are essential to overcome current obstacles in managing infectious disease outbreaks.