Neural network architecture for differentiating Covid19 and viral pneumonia

Covid 19 has wreaked havoc on the world when in some countries had cases in ten thousand each day thus, leading to a load on the healthcare system. Meaning that doctors and nurses had to spend more time on diagnostics. Therefore, one of the methods for reducing this load was to use a neural network for differentiating between covid and pneumonia cases. This citation showcase how neural networks can be used to detect lung x-rays having covid and pneumonia. Recall, precision, and f1-score measures are utilized to optimize the adaptive brightness of the images, selection process, resizing, and tune the neural network architecture parameters or hyperparameters. Classification quality metrics values over 91% depicted a decisive difference between radiographic images of patients having COVID-19 and pneumonia. Making it possible to make a model with strong forecasting capacity without pre-training on data from the 3rd party or engaging ready-to-use complicated neural network models. It can be the next step for the advancement of reliable and sensitive COVID-19 diagnostics.