Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19

Abstract

Differentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT). Method: COVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images. Results: We retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals. Conclusions: The developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.

Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease

“…Interestingly, five of the seven features included in the top 5% predictive features of our study were texture related, thus indicating that the lesion hetereogeneity may help the differential diagnosis of GGOs. These results are in line with recent studies on the differential diagnosis of COVID-19 pneumonia 21 , 54 , 55 . For instance, Gulbay et al showed that the mean skewness and texture features were significantly different in GGOs when comparing COVID-19 and atypical pneumonia 21 .…”

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

“…As demonstrated by Wu et al [ 26 ] CTTA could be used to rapidly differentiate COVID-19 from other infectious pneumonia. However, radiomic signature has been proven effective in classifying between stable and progressive group of COVID-19 patients with an AUC, sensitivity and specificity of 0.8, 81, and 74%, respectively [ 25 ].…”

“…Radiomics machine learning-based tool has been recently studied to evaluate the severity of COVID-19 [ 25 ]. A recent study proposed by Wu et al [ 26 ] have shown that despite the similar clinical and radiological manifestations in COVID-19 and non-COVID-19 patients, CT texture features may be a helpful toll to differentiate these two population. As a noninvasive and rapid imaging biomarker, texture analysis could improve COVID-19 chest CT diagnosis reducing false positive with the similar lung imaging pattern and allowing a better management of the disease.…”

A transfer learning-based deep learning approach for automated COVID-19 diagnosis with audio data

“…The performance of their ResNet50 deep model was compared by the performances of two radiologists, and their deep learning approach was shown to be better than human readers with 95.6% AUC. Different from the unsupervised DL approach for the problem, Wu et al [9] followed the supervised methodology. The texture features of CT scans were extracted by experienced radiologists.…”