Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images
- DOI
- 10.1016/j.chemolab.2022.104539
- Published
- 2022-05
- Container
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.chemolab.2022.104539,
title = {Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images},
author = {Narin Aslan and Gonca Ozmen Koca and Mehmet Ali Kobat and Sengul Dogan},
year = {2022},
journal = {Chemometrics and Intelligent Laboratory Systems},
doi = {10.1016/j.chemolab.2022.104539},
url = {https://doi.org/10.1016/j.chemolab.2022.104539}
}RIS
TY - JOUR TI - Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images AU - Narin Aslan AU - Gonca Ozmen Koca AU - Mehmet Ali Kobat AU - Sengul Dogan PY - 2022 JO - Chemometrics and Intelligent Laboratory Systems DO - 10.1016/j.chemolab.2022.104539 UR - https://doi.org/10.1016/j.chemolab.2022.104539 ER -
APA
Aslan, N., Koca, G. O., Kobat, M. A., & Dogan, S. (2022). Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2022.104539
Source records
- crossref · retrieved 2026-09-27T05:56:17.602Z