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EffViT-COVID: A dual-path network for COVID-19 percentage estimation
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India.
Insights
This study introduces a deep learning framework to estimate COVID-19 infection percentage from CT scans. The novel approach combines vision transformers and CNNs for accurate COVID-19 diagnosis and severity assessment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- The COVID-19 pandemic caused a global health crisis, necessitating advanced diagnostic tools.
- Accurate estimation of COVID-19 infection percentage is crucial for patient management and treatment planning.
- Existing vision-based methods for COVID-19 prediction lack focus on infection percentage estimation.
Purpose of the Study:
- To propose a novel deep learning framework for estimating COVID-19 infection percentage.
- To enhance the precision of COVID-19 diagnosis by quantifying infection severity.
- To develop a robust tool for assessing lung involvement in COVID-19 patients.
Main Methods:
- A deep learning network integrating features from vision transformers and EfficientNet-B7 (CNN).
- Feature fusion technique to create an information-rich vector for precise estimation.
- Evaluation on the Per-COVID-19 dataset using slice-level metrics (PC, MAE, RMSE) and 5-fold cross-validation.
Main Results:
- The proposed network achieved state-of-the-art performance in COVID-19 infection percentage estimation.
- Achieved high Pearson correlation coefficient (PC), low Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
- Demonstrated a low overall average difference between actual and predicted infection percentages, indicating high accuracy.
Conclusions:
- The developed deep learning framework is robust and efficient for estimating COVID-19 infection percentage.
- The fusion of vision transformers and CNNs significantly improves estimation accuracy.
- This work contributes a valuable tool for objective assessment of COVID-19 severity using CT imaging.
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 , , and , 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 . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.
