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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Priya Aggarwal1, Narendra Kumar Mishra2, Binish Fatimah3

  • 1The Vehant Technology Pvt. Ltd., Noida, India.

Computers in Biology and Medicine
|March 19, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning models, particularly Convolutional Neural Networks (CNNs), show promise for automated COVID-19 detection using Chest X-Ray (CXR) and CT scans. This review explores AI advancements, challenges, and future research in medical image classification for COVID-19 diagnosis.

Keywords:
COVID-19 detectionConvolutional neural networksDeep learningX-ray and CT scan Images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • COVID-19, caused by SARS-CoV-2, is a global health crisis.
  • Radiological imaging (CXR, CT) is crucial for COVID-19 diagnosis.
  • Manual interpretation of medical images is time-consuming and error-prone.

Purpose of the Study:

  • To review Deep Learning (DL) based methods for COVID-19 classification using CXR and CT images.
  • To summarize state-of-the-art AI advancements in COVID-19 detection.
  • To discuss open challenges and future research directions in AI-driven medical image analysis.

Main Methods:

  • Review of significant research publications on DL for COVID-19 classification.
  • Focus on Convolutional Neural Networks (CNNs) applied to CXR and CT data.
  • Analysis of current AI techniques and their effectiveness.

Main Results:

  • DL, especially CNNs, offers automated and accurate COVID-19 detection from radiological images.
  • Significant progress has been made in AI-based diagnostic tools.
  • Challenges remain in standardization, data variability, and clinical integration.

Conclusions:

  • DL-based image classification is a powerful tool for COVID-19 diagnosis.
  • Further research is needed to address existing challenges and enhance clinical utility.
  • Future directions include improving model robustness and exploring novel AI architectures.