Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.
In late 2019, a new type of coronavirus emerged in China and was named SARS-CoV-2. It first impacted the country where it emerged and then spread around the world. SARS-CoV-2 is the cause of COVID-19 disease that leaves characteristic impressions on chest CT images of infected patients. In this article, we propose a classification model, based on CNN and wavelet transform, to classify images of COVID-19 patients. It was named WCNN-COVID. The model was applied and tested in open and private TC image repositories. A total of 25534 images of 200 patients were processed. The confusion matrix was generated by calculating Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp). The Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUCs) were also plotted and used for evaluation. Metric results were ACC = 0.9950, Sen = 99.16% and Sp = 99.89%.
SARS-CoV-2 is the causative agent of COVID-19 and leaves characteristic impressions on chest Computed Tomography (CT) images in infected patients and this analysis is performed by radiologists through visual reading of lung images, and failures may occur. In this article, we propose a classification model, called Wavelet Convolutional Neural Network (WCNN) that aims to improve the differentiation of images of patients with COVID-19 from images of patients with other lung infections. The WCNN model was based on a Convolutional Neural Network (CNN) and wavelet transform. The model proposes a new input layer added to the neural network, which was called Wave layer. The hyperparameters values were defined by ablation tests. WCNN was applied to chest CT images to images from two internal and one external repositories. For all repositories, the average results of Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp) were calculated. Subsequently, the average results of the repositories were consolidated, and the final values were ACC = 0.9819, Sen = 0.9783 and Sp = 0.98. The WCNN model uses a new Wave input layer, which standardizes the network input, without using data augmentation, resizing and segmentation techniques, maintaining the integrity of the tomographic image analysis. Thus, applications developed based on WCNN have the potential to assist radiologists with a second opinion in the analysis.1
INTRODUCTION: The identification of COVID-19 pneumonia using chest radiography is challenging. OBJECTIVES: We investigate classification models to differentiate COVID-19-based and typical pneumonia in chest radiography. METHODS: We use 136 segmented chest X-rays to train and evaluate the performance of support vector machine (SVM), random forest (RF), AdaBoost (AB), and logistic regression (LR) classification methods. We use the PyRadiomics to extract statistical texture-based features in the right, left, and in six lung zones. We use a stratified k-folds (k=5) cross-validation within the training dataset, selecting the most relevant features with validation accuracy and relative feature importance. RESULTS: The AB model seems to be the best discriminant method, using six lung zones (AUC = 0.98). CONCLUSION: Our study shows a predominance of radiomic texture-based features related to COVID-19 pneumonia within the right lung, with a tendency within the upper lung zone.
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