Densely connected convolutional networks-based COVID-19 screening model

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 5; pp. 3044 - 3051
• Main Authors: Singh, Dilbag, Kumar, Vijay, Kaur, Manjit
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial Intelligence Applications for COVID-19; Chest; Classification; Computed tomography; Computer Science; Control; Coronaviruses; COVID-19; Detection; Diagnosis; Indexing; Machine learning; Machines; Manufacturing; Mechanical Engineering; Medical imaging; Model accuracy; Polymerase chain reaction; Prediction; Processes; Screening; Sensitivity; Tuberculosis; Deep learning; COVID-19; Chest CT; Transfer learning
• ISSN: 0924-669X; 1573-7497; 1573-7497
• DOI: 10.1007/s10489-020-02149-6

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.