SUMEETRM/covid19-ai

The 2019 novel coronavirus (COVID-19) presents several unique features. While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye. Bilateral multiple lobular and subsegmental areas of consolidation can be observed in COVID-19 patients. The following model aims to present a neural network aimed to detect COVID-19 cases through chest X-Rays. While a neural network to detect COVID-19 cases has been published, the following model has a much higher accuracy. Out model uses a sample of 100 COVID-19 positive cases and 100 COVID-19 negative cases, and has an accuracy of 91% . Of the true positive patients, the model had an accuracy of 100%, and of the true negative patients, the model had an accuracy of 80%. Given the fact that the data is limited, the model can be improved in the near future for rapid diagnosis of COVID-19 cases with an extremely low rate of false positives. While the model may still be in an initial phase of development, it can be put to practical use soon with enough training and accuracy.

PythonGPL-3.0