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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 02

Published on: February 2026

EFFECTIVE DEEP LEARNING METHODS AND FEATURE EXTRACTION FOR IMPROVING ENHANCE AND EFFICIENT COVID-19 CHEST XRAY IMAGES CLASSIFICATION.

Chander Deep Singh

Dr. Neha Tuli

Sri Sai College of Engineering & Technology Badhani Pathankot Punjab (India)

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

An outbreak of unidentified infections known as COVID-19 primarily affected the circulatory tract. The illness spreads throughout the entire world and eventually gets worse significantly declining population. The well developed & perfected Covid-19 prediction method is still debatable. In this article, we suggested using affordable lots of X-ray images scans to diagnose COVID-19 patients. The majority of healthcare facilities have access to X-ray imaging contrasted to other imaging techniques. By employing deep learning models CNN with the goal of analyzing its regular exponential behaviour as well as making predictions about the COVID-2019's potential reach across countries by using real-time data.We aim to enhance COVID-19 classification accuracy and efficiency. On the premise of accuracy, class specifications TP rate, FP rate, precision, recall, as well as F-measure, the suggested CNN is validated to other existing methods Naive Bayes, Support Vector Machine, Random Forest, as well as decision tree. The suggested method's accuracy is 99.07. percent. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.

How to Cite this Paper

Singh, C. D. (2026). Effective Deep Learning Methods and Feature Extraction for Improving Enhance and Efficient Covid-19 Chest Xray Images Classification.. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(02). https://doi.org/10.55041/ijcope.v2i2.014

Singh, Chander. "Effective Deep Learning Methods and Feature Extraction for Improving Enhance and Efficient Covid-19 Chest Xray Images Classification.." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 02, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i2.014.

Singh, Chander. "Effective Deep Learning Methods and Feature Extraction for Improving Enhance and Efficient Covid-19 Chest Xray Images Classification.." International Journal of Creative and Open Research in Engineering and Management 02, no. 02 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i2.014.

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Ethical Compliance & Review Process

  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
  • Authors retain copyright.
  • Peer Review Type: Double-Blind Peer Review
  • Published on: Feb 28 2026
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