Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study

Abstract

Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia. Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images. Methods: Chest X-ray images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis. Results: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest Area Under the Curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity. Conclusion: Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

“…The proposed method with Res-net 34 achieved 98.33% accuracy. Armando Ugo Cavallo et al [ 13 ] extracted 308 texture features per ROI from chest X-ray and analyzed pneumonia infection in COVID-19 patients using machine-learning models. Metaheuristic algorithms [ 14 ] are becoming more popular because they do not require gradient information, have ability to avoid entrapment into local optima, and are utilized to solve complex optimization problems efficiently.…”

Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension

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A Metabolomics-Based Screening Proposal for Colorectal Cancer

“…The 10 individual classification models were also "ensembled" according to a voting scheme that used both the cross-validation accuracy and the confidence (i.e., distance from classification margin) as a vote weight. Ensembling was executed in accordance with Troisi et al [56,57,[62][63][64][65]. In brief, for samples identified as "CRC", the scores (obtained by multiplying the model cross-validation accuracy and classification confidence) were used as is, whereas the scores of "CTRL" samples were multiplied by −1.…”