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COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques.

Abul Bashar1, Ghazanfar Latif2,3, Ghassen Ben Brahim3

  • 1Department of Computer Engineering, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia.

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This study introduces an optimized Deep Learning model for rapid COVID-19 pneumonia diagnosis from X-rays. The novel approach achieved 95.63% accuracy, outperforming existing methods for efficient and cost-effective detection.

Keywords:
COVID-19 detectionchest X-rayconvolutional neural networkslung opacity detectionviral pneumonia detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • COVID-19 vaccines reduce severity but do not prevent infection, necessitating adaptive management strategies.
  • Manual diagnosis of COVID-19 pneumonia from X-rays is labor-intensive, costly, and time-consuming.
  • Automated diagnostic systems offer real-time, cost-effective solutions for COVID-19 detection.

Purpose of the Study:

  • To propose a novel, optimized Deep Learning (DL) approach for automatic COVID-19 pneumonia classification and diagnosis using X-ray images.
  • To enhance the accuracy and efficiency of COVID-19 diagnosis through advanced image processing and machine learning techniques.

Main Methods:

  • Utilized a publicly available Kaggle dataset of 21,165 chest X-rays (Normal, COVID-19, Lung Opacity, Viral Pneumonia).
  • Implemented a three-stage DL process: Image Enhancement, Data Augmentation, and Transfer Learning (AlexNet, GoogleNet, VGG16, VGG19, DenseNet).
  • Employed VGG16 with frozen weights on an augmented, enhanced dataset for classification.

Main Results:

  • Achieved a highest classification accuracy of 95.63% using the VGG16 transfer learning algorithm.
  • The proposed DL approach demonstrated superior performance compared to existing methods in recent literature.
  • Results indicate significant potential for automated, accurate COVID-19 pneumonia detection.

Conclusions:

  • The optimized Deep Learning approach offers a highly accurate and efficient method for diagnosing COVID-19 pneumonia from X-ray images.
  • This research contributes valuable insights into leveraging AI for improved public health diagnostics.
  • Future work will focus on correlating DL model results with clinical observations to further refine diagnostic accuracy.