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%.