Towards robust diagnosis of COVID-19 using vision self-attention transformer

• Published in: Scientific reports Vol. 12; no. 1; pp. 8922 - 12
• Main Authors: Mehboob, Fozia, Rauf, Abdul, Jiang, Richard, Saudagar, Abdul Khader Jilani, Malik, Khalid Mahmood, Khan, Muhammad Badruddin, Hasnat, Mozaherul Hoque Abdul, AlTameem, Abdullah, AlKhathami, Mohammed
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 692/699/255; 692/700/139; Algorithms; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnosis; Datasets; Deep learning; Diagnosis; Humanities and Social Sciences; Humans; Medical imaging; multidisciplinary; Neural networks; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted - methods; SARS-CoV-2; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Training
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-13039-x

The outbreak of COVID-19, since its appearance, has affected about 200 countries and endangered millions of lives. COVID-19 is extremely contagious disease, and it can quickly incapacitate the healthcare systems if infected cases are not handled timely. Several Conventional Neural Networks (CNN) based techniques have been developed to diagnose the COVID-19. These techniques require a large, labelled dataset to train the algorithm fully, but there are not too many labelled datasets. To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the performances of self-attention transformer-based approach with CNN and Ensemble classifiers for diagnosis of COVID-19 using binary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and multi-class Hybrid-learning for UnbiaSed predicTion of COVID-19 (HUST-19) CT scan dataset. To perform this comparison, we have tested Deep learning-based classifiers and ensemble classifiers with proposed approach using CT scan images. Proposed approach is more effective in detection of COVID-19 with an accuracy of 99.7% on multi-class HUST-19, whereas 98% on binary class SARS-CoV-2 dataset. Cross corpus evaluation achieves accuracy of 93% by training the model with Hust19 dataset and testing using Brazilian COVID dataset.