An interpretable COVID-19 chest X-ray classification through transfer learning and discriminative localisation-based deep learning techniques
by Lavanya Yamathi; K. Sandhya Rani
International Journal of Smart Grid and Green Communications (IJSGGC), Vol. 2, No. 2, 2022

Abstract: The present time is marked by the upsurge of coronavirus (COVID-19) pandemic, which persists and has catastrophic consequences on global health and well-being. In addition to RT-PCR test, CT scan and chest X-ray have become essential in detecting and treating COVID-19 patients. Several deep learning frameworks have been put forward in recent times for the COVID-19 chest X-ray classification. Therefore, to overcome the challenges of data scarcity and lack of interpretability, also to increase the performance of COVID-19 chest X-ray classification, a first of its kind of model is proposed in which transfer learning (TL) and discriminative localisation (DL) are successfully adopted. To verify the superior classification performance of the TL-DL-based C-19CXC model, a set of experiments are conducted on widely used eight pre-trained models like MobileNet, SqueezeNet, etc., on publicly available large datasets. The MobileNet based model outcome accuracy is 98.73% followed by Xception-based framework with 98.34% accuracy.

Online publication date: Wed, 04-Jan-2023

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