Performance Analysis of Deep Neural Networks for Covid-19 Detection from Chest Radiographs

Contrary to the World Health Organization's (WHO) and the medical community's projections, Covid-19, which started in Wuhan, China, in December 2019, still doesn't show any signs of progressing to the endemic stage or slowing down any time soon. It continues to wreak havoc on the lives and livelihood of thousands of people every day. There is general agreement that the best way to contain this dangerous virus is through testing and isolation. Therefore, in these epidemic times, developing an automated Covid-19 detection method is of utmost importance. This study uses three different Machine Learning classifiers, such as Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), along with five Transfer Learning models such as DenseNet121, DenseNet169, ResNet50, ResNet152V2, and Xception as feature extraction methods for identifying Covid-19. Five different datasets are used to assess the models' performance to generalize. There are encouraging findings, with the best one being the combination of DenseNet121 and DenseNet169 together with SVM and LR.