Publication date: Oct 27, 2024
This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X-ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID-19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state-of-the-art models and a lightweight CNN architecture. The results showed that the proposed method achieves high accuracy values (Accuracy): 0. 95 for COVID-19, 0. 89 for pneumonia, and 0. 92 for normal images (p
| Concepts | Keywords |
|---|---|
| Architecture | diagnosis |
| Clinics | diagnostic efficiency |
| Cnn | EfficientNet‐B0 |
| Pneumonia | support vector machines |
| transfer learning |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | IDO | algorithm |
| disease | MESH | pneumonia |
| disease | MESH | Long Covid |