xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

• Published in: IEEE journal of translational engineering in health and medicine Vol. 10; pp. 1 - 10
• Main Authors: Mondal, Arnab Kumar, Bhattacharjee, Arnab, Singla, Parag, Prathosh, A. P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI for COVID-19 detection; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; CT scan and CXR; Deep Learning; Feature extraction; Humans; Medical imaging; Predictive models; Radiography; Radiography, Thoracic; SARS-CoV-2; Transformers; Viral diseases; Vision; vision transformer; X-ray imaging; X-Rays; deep learning; AI for COVID-19 detection; CT scan and CXR; vision transformer
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2021.3134096

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS . Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.