Computational intelligence in the identification of Covid-19 patients by using KNN-SVM Classifier
Initiatives to mitigate the persistent coronavirus disease 2019 (COVID-19) crisis shown that quick, sensitive, and extensive screening is essential for managing the present epidemic and future pandemics. This virus seeks to infect the lungs by generating white, patchy opacities inside them. This re...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
College of Computer and Information Technology – University of Wasit, Iraq
2024-12-01
|
| Series: | Wasit Journal of Computer and Mathematics Science |
| Subjects: | |
| Online Access: | http://wjcm.uowasit.edu.iq/index.php/wjcm/article/view/306 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Initiatives to mitigate the persistent coronavirus disease 2019 (COVID-19) crisis shown that quick, sensitive, and extensive screening is essential for managing the present epidemic and future pandemics. This virus seeks to infect the lungs by generating white, patchy opacities inside them. This research presents an advanced methodology employing deep learning techniques for the analysis of medical pictures pertaining to respiratory disorders. This experiment included two data sets, the initial one including normal lungs sourced from the Kaggle data pool. We acquired the anomalous lungs from https://github.com/muhammedtalo/COVID-19. We applied Principal Component Analysis (PCA) and Histogram of Gradients (HOG) as extract features. while we conducted a classification process using K nearest neighbors (KNN) and Support Vector Machine (SVM) algorithms . Results showed that the classification accuracy with SVM for Covid-19 identification is 88.54% while with KNN is 82.31%
|
|---|---|
| ISSN: | 2788-5879 2788-5887 |