Detection of Covid-19 From an Imbalanced Chest X-ray Image Data Set

The Covid-19 pandemic has spread quickly, making identification of the virus critically important in assisting overburdened healthcare systems. Numerous techniques have been used to identify Covid-19, of which the Polymerase chain reaction (PCR) test is the most common. However, obtaining results from the PCR test can take up to two days. An alternative is to use X-ray images of the subject's chest area as inputs to a deep learning neural networks algorithm. The two problems with this approach are the choice of architecture and the method used to deal with the imbalanced data. In this study a comparative analysis of a standard convolutional neural network (CNN) and a number of transfer learning algorithms with a range of imbalanced data techniques was conducted to detect Covid-19 from a data set of chest x-ray images. This data set was an amalgamation of two data sets extracted from the Kaggle Covid-19 open source data repository and non-Covid illnesses taken from the National Institute of Health. The resulting data set was had over 115k records and 15 different type of findings ranging from no-illness to illnesses such as Covid-19, emphysema and lung cancer. This study addresses the problem of class imbalance on the largest data set used for x-ray detection of Covid-19 by combining undersampling and oversampling methods. The results showed that a CNN model in conjunction with these random over and under sampling methods outperformed all other candidates when identifying Covid-19 with a F1-score of 93%, a precision of 90% and a recall of 91%. © 2021 CEUR-WS. All rights reserved.

Coronavirus: An emergency for healthcare professionals.

Coronavirus infection is a transmissible disease. It was first described in China in December, 2019. It has been said to have a person-to-person transmission after prolonged and unprotected exposure. Patients with a potential SARS-CoV-2 exposure present with symptoms of low-grade pyrexia, dry cough, or shortness of breath. People with these symptoms should contact health-care providers before seeking medical intervention so that appropriate preventive actions may be implemented. Health-care facilities should rapidly isolate suspected individuals and notify local health departments for support involved in performing laboratory tests and efforts in containment. The present article describes the nature of virus, method of detection, and its mode of transmission.