CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformers

TL;DR

This paper introduces a Compact Convolutional Transformer model for automatic COVID-19 detection from chest X-ray images, achieving high accuracy and addressing challenges in rapid diagnosis during the pandemic.

Contribution

The paper presents a novel transformer-based approach specifically designed for COVID-19 detection from X-ray images, demonstrating superior accuracy over previous methods.

Findings

Achieved 98% accuracy in COVID-19 detection

Outperformed existing models in accuracy

Validated effectiveness through extensive experiments

Abstract

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 98% which outperforms the previous works.