The deep transfer learning model is used to classify COVID-19 infected patients by considering their chest CT images.
The cost-sensitive top-2 smooth loss function is also utilized to enhance the results further.
The deep transfer learning model is trained on a benchmark open dataset of chest CT images.
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.Le texte complet de cet article est disponible en PDF.
Keywords : Deep learning, COVID-19, Disease, Classification, Chest CT images