The year 2019 ended by giving healthcare systems worldwide a catastrophic blow, leaving hospitals flooded with patients, doctors overburdened, and distressing mortality rates due to the unforeseen spread of COVID-19. On contemplating the events of the last two years and a half, hospitals that required a shorter duration to confirm the diagnosis were able to save significantly more lives. One major issue hindering the efficiency of the diagnosis process was the primary method of diagnosis, the RT-PCR test. It is time-consuming, costly, and not readily available, especially in rural areas. Consequently, physicians started relying heavily on other diagnostic tools such as chest x-rays. A web tool was developed to help physicians upload and analyze x-ray images quickly using several machine learning models in the backend to diagnose COVID-19 or pneumonia with its types. This was attained using multiple datasets and techniques such as deep learning models and pre-trained models. The web tool includes two classifications, one differentiates normal, COVID-19, and pneumonia (PCN), and the second differentiates viral and bacterial pneumonia (BV). Six pre-trained models were used for each classification with average 95 percent testing accuracy for PCN and 85 percent for BV. Our own proposed model had a 90 percent testing accuracy for PCN and 80 percent for BV. The results presented in this paper can be used to aid in ameliorating the efficiency of healthcare systems in the diagnosis of COVID-19 and pneumonia.

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