Integrating Data Augmentation with AdaBoost for Effective COVID-19 Pneumonia Classification

Clicks: 1
ID: 312941
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

Ranked #409 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
In the COVID-19 pandemic, the urgent need for precise pneumonia diagnosis has prompted this research to propose a customized AdaBoost algorithm tailored for classifying COVID-19 pneumonia cases. The study follows a structured framework encompassing four primary supervised learning stages: data acquisition, preprocessing, supervised learning with augmented data, and rigorous performance evaluation. Leveraging a comprehensive GitHub dataset and Python in Anaconda Jupyter Notebook, advanced preprocessing techniques are employed to optimize data for machine learning algorithms. The AdaBoost algorithm, enhanced with data augmentation methods, is deployed to bolster model robustness and performance. The model demonstrates superior effectiveness with an average accuracy rate of 84.49%, surpassing existing methodologies. This performance underscores its potential in addressing public health challenges associated with pneumonia diagnosis during the COVID-19 crisis. This research introduces an optimized application of AdaBoost for pneumonia classification, validated across diverse datasets, ensuring reliable disease classification and predictive modeling capabilities to anticipate future trends. These insights are pivotal for guiding public health interventions and optimizing resource allocation, marking significant advancements in diagnostic accuracy and patient care during the pandemic.
Reference Key
imported_1777057962_69ebc0aadc924 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Suliman, Fazal Malik, Muhammad Qasim Khan, Irfan Ullah, Abd Ur Rub
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/512
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.