A Two-Dimensional Sparse Matrix Profile DenseNet for COVID-19 Diagnosis Using Chest CT Images

COVID-19 is a newly identified disease, which is very contagious and has been rapidly spreading across different countries around the world, calling for rapid and accurate diagnosis tools. Chest CT imaging has been widely used in clinical practice for disease diagnosis, but image reading is still a time-consuming work. We aim to integrate an image preprocessing technology for anomaly detection with supervised deep learning for chest CT imaging-based COVID-19 diagnosis. In this study, a matrix profile technique was introduced to CT image anomaly detection in two levels. At one-dimensional level, CT images were simply flatted and transformed to a one-dimensional vector so that the matrix profile algorithm could be implemented for them directly. At two-dimensional level,a matrix profile was calculated in a sliding window way for every segment in the image. An anomaly severity score (CT-SS) was calculated, and the difference of the CT-SS between the COVID-19 CT images and Non-COVID-19 CT images was tested. A sparse anomaly mask was calculated and applied to penalize the pixel values of each image. The anomaly weighted images were then used to train standard DenseNet deep learning models to distinguish the COVID-19 CT from Non-COVID-19 CT images. A VGG19 model was used as a baseline model for comparison. Although extra finetuning needs to be done manually, the one-dimensional matrix profile method could identify the anomalies successfully. Using the two-dimensional matrix profiling method, CT-SS and anomaly weighted image can be successfully generated for each image. The CT-SS significantly differed among the COVID-19 CT images and Non-COVID-19 CT images ($p-value <; 0.05$ ). Furthermore, we identified a potential causal association between the number of underlying diseases of a COVID-19 patient and the severity of the disease through statistical mediation analysis. Compared to the raw images, the anomaly weighted images showed generally better performance in training the DenseNet models with different architectures for diagnosing COVID-19, which was validated using two publicly available COVID-19 lung CT image datasets. The metric Area Under the Curve(AUC) on one dataset were 0.7799(weighted)vs. 0.7391(unweighted), 0.7812(weighted) vs. 0.7410(unweighted), 0.7780(weighted) vs. 0.7399(unweighted), 0.7045(weighted) vs. 0.6910(unweighted) for DenseNet121, DenseNet169, DenseNet201, and the baseline model VGG19, respectively. The same trend was observed using another independent dataset. The significant results revealed the critical value of using this existing state-of-the-art algorithm for image anomaly detection. Furthermore, the end-to-end model structure has the potential to work as a rapid tool for clinical imaging-based diagnosis.