Pneumonia Detection with Different Classifiers in the Hybrid Model Approach
Özet
Pneumonia is one of the dangerous diseases caused by bacteria or viruses, whose
early diagnosis is important. Various methods are used to diagnose pneumonia: blood
culture, sputum culture, fluid sample, bronchoscopy, pulse oximetry, and chest Xray. Chest X-ray is one of the most widely used methods of detecting pneumonia.
Assisted diagnosis systems are being created for automatic pneumonia detection
using chest X-ray images. In this study, the hybrid model approach in pneumonia
detection is compared with different classifiers. For this purpose, X-Ray data set
from pneumonia and non-pneumonia chest X-ray images was used. By taking the
features from the penultimate fully connected layer of Alexnet, VGG16 and VGG19,
which are transfer learning methods, classification is made with Naive Bayes,
Decision tree, KNN and SVM classifier. In the classification made with the
VGG16+DVM hybrid model, the accuracy was calculated as 97.61%, the sensitivity
as 98.48%, the selectivity as 95.27% and the F1 score as 98.36%.
Atıf için künye
Kılıç, R., Sağlam, H. K., Oral, E. A., & Özbek, İ. Y. (2022). Pneumonia Detection with Different Classifiers in the Hybrid Model Approach. 4th International Conference on Advanced Engineering Technologies Ͷ , Bayburt (ss. 170–176).
BibTeX
@inproceedings{kilic2022-181078,
author = {Kılıç, Rabiye and Sağlam, Hilal Kübra and Oral, Emin Argun and Özbek, İbrahim Yücel},
title = {Pneumonia Detection with Different Classifiers in the Hybrid Model Approach},
booktitle = {4th International Conference on Advanced Engineering Technologies Ͷ },
address = {Bayburt},
year = {2022},
pages = {170--176},
url = {https://akademik.atauni.edu.tr/yayinlar/181078-pneumonia-detection-with-different-classifiers-in-the-hybrid-model-approach}
}