Diagnose COVID-19 Based on CT Images Using Transfer Learning

ABSTRACT

Coronavirus disease is an ongoing pandemic caused by a virus called severe acute respiratory syndrome coronavirus 2. Due to the current global pandemic's perilous state, getting a speedy and precise diagnosis of COVID-19 for everyone who wants to have a COVID-19 test should be the priority. Therefore, building the AlexNet model, which is trained for diagnosing COVID-19 based on CT scans from a large dataset which is composed of 104,009 CT slices coming from 1,489 patients (accuracy is around 67.9%) and a small dataset which is composed of 349 CT images from 216 patients (accuracy is around 62.3 %) would have important implications to help early identification of COVID-19. Moreover, due to the lack of CT scans of positive COVID-19 patients, transferring the learned model parameters from a large dataset to a small dataset contributes to better performance on a small dataset. In our model, the effectiveness of transfer learning is proved by a 1.9% increase in the accuracy of a small dataset. © 2021 IEEE.