Detection of SARS-CoV-2 Virus Using Lightweight Convolutional Neural Networks

A highly contagious illness caused by the SARS-CoV-2 virus pandemic is proven to wreak havoc on people's health and well-being all over the globe. Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) is the source of COVID-19. Chest radiography is one of the most crucial databases for applying detection techniques. COVID-19 infects the respiratory system and replicates, affecting the alveoli as well. Conventional approaches, such as RT-PCR tests, rapid antigen tests, serological tests, etc., are generally used to detect COVID-19 and have proven costly and time-consuming. Several suggested artificial intelligence (AI)-based models for detecting COVID-19 in contaminated individuals use lung ultrasound images, voice patterns, chest sounds, etc. In this paper, we have proposed a lightweight CNN model with a ResNet50 modified configuration that has been used to identify COVID-19 cases using features and variations in the chest radiography image dataset. The empirical results and comparative analysis with the ResNet 101 model prove the lightweight nature of the proposed CNN model. A chest radiography dataset containing COVID-19-infected, normal, and pneumonia-infected images. The dataset comprised almost thousands of chest radiography images from patients from two open-access information standard repositories. The proposed lightweight model gives approximately 97% accuracy by combining the adopted CNN and ResNet50 algorithms.