Deep learning for COVID-19 detection based on CT images

COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.

Automated summary

COVID-19 has had a significant impact globally, with CT images proving to complement RT-PCR testing. The study employed a convolutional neural network for COVID-19 testing and found that larger, out-of-field datasets improve model performance. Transfer learning approach was more successful than current methods, achieving state-of-the-art performance in identification.