Covid-19 Detection using X-Ray Images and Fake X-Ray Detection

The infectious disease COVID-19 has propagated around the Globe in 2019, including to Indonesia. Due to the lack of a vaccine to either treat or stop the spread of the virus, this worldwide pandemic turns into a deadly epidemic. Given that the Rapid Test is more affordable than the SWAB test, it is chosen as the primary method for finding Covid-19 in Indonesia. The hospital's Covid-19 test has a limited capacity due to the rise in Covid-19 patients, thus the newest technology is required to identify and treat this pandemic problem. Thus, the objective of the current study is to evaluate more than hundred X-ray chest images of Covid-19 patients and more than hundred X-ray chest images of healthy individuals. Nowadays, fraudulent photos are widely disseminated through digital media. The exposure of image-based cybercrimes requires the discovery of such phony pictures. In this digital world, research on forging photos and spotting them has promise. By employing a neural network to identify the areas of the picture that have been altered and to show the portions of the original image, the tampered photos are identified. It may be used on the Android platform and afterwards made available to regular users. By applying error level analysis, it is possible to identify the foreign material in a phony picture by comparing its compression ratio to that of the real image. Image metadata is a characteristic that is utilized in conjunction with compression ratio. Although it is possible to modify metadata content to make it unreliable on its own, it is used here as a supporting parameter for the ELA decision.