Supervised framework for COVID-19 classification and lesion localization from chest CT



Background: Quick and precise identification of people suspected of having COVID-19 plays a key function in
imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but
also helps in the development of an improved health system. Building a deep-learning framework for automated
identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic.
Aim: To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and
localization of swellings.

Methods: In all cases, subjects’ chest areas were segmented by means of a pre-trained U-Net; the segmented 3D
chest areas were submitted as inputs to a 3D deep neural network to forecast the likelihood of infection with
COVID-19; the swellings were restricted by joining the initiation areas within the classification system and the
unsupervised linked elements. A total of 499 3D CT scans were utilized for training worldwide and 131 3D CT
scans were utilized for verification.

Results: The algorithm took only 1.93 seconds to process the CT amount of a single affected person using a
special graphics processing unit (GPU). Interesting results were obtained in terms of the development of societal
challenges and better health policy.

Conclusions: The deep-learning model can precisely forecast COVID-19 infectious probabilities and detect
swelling areas in chest CT, with no requirement for training swellings. The easy-to-train and high-functioning
deep-learning algorithm offers a fast method to classify people affected by COVID-19, which is useful to monitor
the SARS-CoV-2 epidemic. [Ethiop. J. Health Dev. 2020; 34(4):235-242]

Key words: COVID-19, CT scan, deep learning, neural network, DeCoVNet, RT-PCR, computed tomography




How to Cite

Junyong Zhang, Yingna Chu, & Na Zhao. (2020). Supervised framework for COVID-19 classification and lesion localization from chest CT. The Ethiopian Journal of Health Development, 34(4). Retrieved from



Original Articles