Smart Covid-19 detection using intelligent computational techniques

Since 2019, people around the world have been suffering from severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Most countries, even developed ones, have struggled to deal with the pandemic. The test is the first way to identify patients affected by the coronavirus. Post-test confirmation in particular takes longer and isn’t as accurate because we’ve seen a lot of tests test positive and then confirm negative with a percentage error of +10% to-10%. There are many respiratory diseases including COPD (chronic obstructive pulmonary disease), lung cancer, pneumonia, bronchitis and Covid-19. According to research, pneumonia and Covid-19 share many of the same symptoms. So it’s very difficult to find out in real time which diseases are affecting patients based on our final test results. Various methods have been used to detect Covid-19 infection. This paper proposes Long Short-Term Memory (LSTM) to improve the effectiveness and efficiency of Covid-19 detection. Several other datasets, notably COVID-19, viral and bacterial pneumonia cases, were used to test the retrained model.