PixNet for early diagnosis of COVID-19 using CT images

Globally, COVID-19 has impacted severely the healthcare systems and the patients as well. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) tests can be effectively supplemented with computed tomography images. Recent research on CT-based screening found that COVID-19 infection is linked to abnormalities in chest Computed Tomography (CT). However, it is difficult to distinguish these from the general abnormalities that are caused in the lungs. Although COVID-19 RT-PCR testing is exceedingly precise, its sensitivity varies based on the sampling technique and the period as well. Some studies have even shown that RT-PCR testing displays very low COVID-19 sensitivity. This motivated the authors to propose a new deep-learning model called PixNet that can detect positive and negative cases accurately. We compared the effectiveness of the proposed model against several state-of-the-art models trained on CT images. On analysis, it is found that the proposed model displays 96% classification accuracy in diagnosing COVID-19 infection. The proposed algorithm automatically detects the infection owing to COVID-19 from CT scan images, which may be an effective screening tool for Clinicians.