| Autor: |
Dwivedi, Pulkit, Padhi, Sandeep, Chakraborty, Soumendu, Raikwar, Suresh Chandra |
| Zdroj: |
Multimedia Tools & Applications; Mar2024, Vol. 83 Issue 10, p30719-30740, 22p |
| Abstrakt: |
Deep Learning models are widely used to address COVID-19 challenges, but they require a large number of training samples. X-ray images of COVID-19 patients are amongst the preferred methods for detection. However, their availability is limited. In contrast, X-ray images of non-COVID-19 patients are available in abundance. Furthermore, COVID-19 patient's treatment varies based on infection severity. This leads to a class imbalance issue as there are far more X-ray images of non-COVID patients than COVID-19 patients available for training deep learning models. As a result, deep learning models cannot achieve the desired levels of accuracy. This study's primary objective is to generate synthetic X-ray images depicting three levels of severity, utilizing a Cycle Consistent Generative Adversarial Network (CycleGAN). The Structural Similarity Index (SSIM) is employed to create training datasets for three severity levels, which are then used to train the corresponding CycleGAN models. Additionally, a comparative analysis is conducted to compare the achieved accuracies between X-ray images of COVID-19 patients and non-COVID-19 patients. This analysis involves datasets containing authentic non-synthetic COVID-19 X-ray images and datasets containing synthetic COVID-19 X-ray images generated using CycleGAN. The results indicate enhanced accuracy when deep models are trained using augmented X-ray data. This study is novel as no prior work has been done on severity-wise dataset generation and classification of COVID-19 X-ray images. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink