Presently, the diagnosis of Corona Virus – 2019 (COVID-19) is a challenging task worldwide as the disease is spreading at a very faster rate. Several people are detected with COVID-19 and the data analyst say that the rate of spread of the disease is increasing exponentially. This investigation has facilitated the need for diagnosing the disease within a short duration of time from the X-ray images of the lungs. Artificial intelligence like deep learning algorithms is deployed to diagnose COVID-19 by maintaining social distancing. Real time data sets are gathered from the government hospitals for healthy as well as those who are affected by COVID-19. On development of a smart phone Application the patients themselves will record the respiratory sounds. The features are extracted using Discrete Wavelet Transform (DWT), where a threshold is applied to extract useful coefficients used to train the Deep learning Neural Networks (DLNN) using Fast Recurrent Convolutional Neural Networks (F-RCNN). The respiratory audio signals are captured to detect patients affected by Corona Virus by a way of non-contact, non-intrusive approach. This mobile phone App is effective in diagnosing the COVID-19 from the X-ray images of the Lungs. Even low income people can also use this technology. The effectiveness of the proposed system which uses DWT and thresholding has a F-measure of 96–98%. The forecasted results were in the range of 89%-95% for the above said algorithms. It is significant from the above results that the severe impact of COVID-19 can be diagnosed using a non-invasive mobile phone App using X-ray images.

• Artificial Intelligence
• respiratory sounds,
• Convolutional NeuralNetwork,
• COVID-19,
• mobile phone App,
• Discrete Wavelet Transform ,
• Thresholding

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