Deep Learning for COVID-19 Diagnosis Using Pretrained and Non-Pretrained Models

Authors

  • Muhammad Basit Umair Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.
  • Muhammad Tufail Department of Computer Science, GPGC Nowshera, Pakistan.
  • Muhammad Asgher Nadeem Department of Computer Science Thal University, Bhakkar, Pakistan.
  • Sajjad Ahmad Solutions Consultant, Emirates Integrated Telecommunications Co, du, UAE.
  • Durr Muhammad Department of Computing, Riphah International College D.G.khan, Pakistan.
  • Maria Khalid Dental Surgeon DHQ Hospital, Bhakkar, Pakistan.
  • Muhammad Azhar Mushtaq Department of Information Technology, University of Sargodha, Sargodha Pakistan.
  • Sadaqat Ali Ramay Department of Computer Science Times institute, Multan, Pakistan.
  • Sayyid Kamran Hussain Department of Computer Science Times institute, Multan, Pakistan.

Keywords:

COVID-19 classification, Feature extraction, Image recognition, CT-scan dataset

Abstract

This article proposes a deep-learning approach to classify COVID-19 cases using image data. Our model uses a convolutional neural network (CNN) to extract features from chest X-rays and classify them as positive or negative for COVID-19. A COVID-19 case dataset is compared to traditional machine learning methods to evaluate model performance. The results obtained demonstrate the effectiveness of the deep learning model in accurately detecting COVID-19 cases with an overall accuracy of 96%. This approach is helpful for rapid and automated diagnosis of COVID-19, especially in resource-limited settings. The proposed method yielded remarkable results compared with recent results.

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Published

2024-03-01

How to Cite

Muhammad Basit Umair, Muhammad Tufail, Muhammad Asgher Nadeem, Sajjad Ahmad, Durr Muhammad, Maria Khalid, Muhammad Azhar Mushtaq, Sadaqat Ali Ramay, & Sayyid Kamran Hussain. (2024). Deep Learning for COVID-19 Diagnosis Using Pretrained and Non-Pretrained Models. Journal of Computing & Biomedical Informatics, 6(02), 413–417. Retrieved from https://jcbi.org/index.php/Main/article/view/395