Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images

Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.