Mondal, AK and Bhattacharjee, A and Singla, P and Prathosh, AP (2022) XViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. In: IEEE Journal of Translational Engineering in Health and Medicine, 10 .
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Abstract
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.
| Item Type: | Journal Article |
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| Publication: | IEEE Journal of Translational Engineering in Health and Medicine |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Deep learning; Diagnosis; Feature extraction; Health care; Viruses; X ray radiography, AI for corona virus disease 2019 detection; Corona virus disease 2019; CT scan and CXR; CT-scan; Deep learning; Features extraction; Predictive models; Transformer; Virus disease; Vision transformer; X-ray imaging, Computerized tomography, human; radiography; X ray, COVID-19; Deep Learning; Humans; Radiography; SARS-CoV-2; X-Rays |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 11 Jul 2022 07:51 |
| Last Modified: | 11 Jul 2022 07:51 |
| URI: | https://eprints.iisc.ac.in/id/eprint/74271 |
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