Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification

Abstract

The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making.

Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening

“…In addition, according to clinical literature on similar studies Xu et al. (2020) ; Gozes, Frid-Adar, Sagie, Zhang, Ji, Greenspan , Gozes, Frid-Adar, Greenspan, Browning, Zhang, Ji, Bernheim, Siegel ; Shi et al. (2021b) : Bilateral multi-focal ground-glass opacities (GGO) in the lower lobes are the most common initial findings on CT, with other characteristics such as pleural thickening less commonly observed in imaging manifestations depending on the severity stage.…”

“…Notably, we consider approaches that use (a) patch-based Wang et al. (2021a) ; Shi et al. (2021b) (b) slice-based Gozes, Frid-Adar, Sagie, Zhang, Ji, Greenspan , Gozes, Frid-Adar, Greenspan, Browning, Zhang, Ji, Bernheim, Siegel ; Hu et al.…”

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

“…The RF model showed encouraging results with an accuracy of 87, 5%. Shi et al proposed an infection Size Aware Random Forest method (iSARF), their method had two steps, the first one consisted of categorizing different groups while the second classified the images [63]. They used an infection size feature defined as the ratio of the volume of infected regions to the total volume of whole segmented lung.…”

A Comparative Study of Classification Approaches for COVID-19 Prediction

“…While differentiating between COVID and Non-COVID, their introduced system achieved 96% accuracy, 95% sensitivity and 96% specificity. Shi et al [12] applied Random Forest (RF) as a machine learning algorithm for screening COVID-19. They utilized CT images of 2685 patients to evaluate their presented model.…”