Impact of Aligning Saliency Maps on COVID-19 Disease Detection Using Chest X-Ray Images

Purwita, A. A.; Qomariyah, N. N.

The Coronavirus disease 2019 (COVID-19) has been spread across the world in the year 2020. During the same period, many deep learning researchers have proposed different screening or diagnostic methods as an alternative to the commonly used method, e.g., reverse-transcriptase polymerase chain reaction (RT-PCR). One of the alternatives is the use of chest X-ray (CXR) images. In this paper, we first highlight the fact that by using public, pretrained deep learning model can yield a bias result. For example, by applying a saliency map, we show that a model point to features that are located outside of the lungs. In addition, by applying multiple saliency maps, differences in locations where a model focuses on can be observed. Therefore, We propose a new loss function where we constraint the saliency maps to converge to the same region. The results show that the proposed method is better compared to the model without alignment, where the F1-score of the proposed model is 91.3% versus 89.2%. © 2021 IEEE.