In Biomedical signal processing and control
COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide and spread widely. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of coronavirus. Computed tomography (CT) scanning has proven useful in diagnosing several respiratory lung problems, including COVID-19 infections. Automated detection of COVID-19 using chest CT-scan images may reduce the clinician's load and save the lives of thousands of people. This study proposes a robust framework for the automated screening of COVID-19 using chest CT-scan images and deep learning-based techniques. In this work, a publically accessible CT-scan image dataset (contains the 1252 COVID-19 and 1230 non-COVID chest CT images), two pre-trained deep learning models (DLMs) namely, MobileNetV2 and DarkNet19, and a newly-designed lightweight DLM, are utilized for the automated screening of COVID-19. A repeated ten-fold holdout validation method is utilized for the training, validation, and testing of DLMs. The highest classification accuracy of 98.91% is achieved using transfer-learned DarkNet19. The proposed framework is ready to be tested with more CT images. The simulation results with the publicly available COVID-19 CT scan image dataset are included to show the effectiveness of the presented study.
Gupta Kapil, Bajaj Varun
COVID-19, CT-scan images, Deep learning, Transfer learning