EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Highlights

• Proposed a dual-path network for COVID-19 percentage estimation.

• An encoder mechanism is employed to effectively extract the rich features set.

• Extensive experiments are conducted to validate the proposed approach.

• Compared to existing methods, proposed approach achieves new state-of-the-art results.

Abstract

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labeled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves $0.9886\pm 0.009$, $1.23\pm 0.378$, and $3.12\pm 1.56$, PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be $<2\text{\%}$. In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.