Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

Abstract

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.

arXiv comment12 pages

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• Can AI help in screening Viral and COVID-19 pneumonia?
• COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
• A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images
• Representation Based Regression for Object Distance Estimation
• Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
• R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
• A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets
• Operational Support Estimator Networks
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging