Deep transfer learning based classification model for COVID-19 disease
Autor
Pathak, Y.
Shukla, P.K.
Tiwari, A.
Stalin, S.
Singh, S.
Shukla, P.K.
Institución
Resumen
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 bilateral 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.