Deep Transfer Learning based Classification Model for COVID-19 Disease

Yadunath Pathak, Prashant Kumar Shukla, Akhilesh Tiwari, Shalini Stalin, Saurabh Singh, Piyush Kumar Shukla
2020 IRBM  
The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the
more » ... change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.
doi:10.1016/j.irbm.2020.05.003 pmid:32837678 pmcid:PMC7238986 fatcat:szexlebuvjgq5bpp5arotmxc2q