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

Clicks: 19
ID: 313107
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #381 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
imported_1777059093_69ebc515a6c3d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Basit Umair, Muhammad Tufail, Muhammad Asgher Nadeem, Sajjad Ahmad, Durr Muhammad, Maria Khalid, Muhammad Azhar Mushtaq, Sadaqat Ali Ramay, Sayyid Kamran Hussain
Are you one of these authors? Claim your profile
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/395
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.