Evaluation of Bag-of-Words Classifiers for COVID-19 Detection from Cough Recordings

The paper assesses the efficiency of bag-of-words classifiers for reliable detection of Covid-19 from cough recordings. The effect of using two distinct encoding strategies and variable codebook dimensions is evaluated in terms of Area Under Curve (AUC), accuracy, sensitivity, and specificity. Three distinct feature extraction procedures are tested, followed by a Support Vector Machine (SVM) classifier. Experiments conducted on two cough recordings datasets indicate that sparse encoding yields best performances, showing robustness against feature type and codebook dimension. © 2022 IEEE.

Evaluation of Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images

The paper presents a comparative analysis of five Convolutional Neural Networks (CNN's) for reliable detection of COVID-19 from chest X-ray (CXR) images. The transfer learning approach is applied to a dataset compiled from public sources, aiming at discriminating the presence of COVID-19 pathology from normal, lung opacity, and pneumonia situations. Experimental results indicate that best accuracy performances are obtained by the Resnet-50 network, while all CNN's compare favorably with existing solutions. © 2021 IEEE.