COVID-19 Diagnosis using Laplacian Edge Detection & Morphological Dilation

COVID-19 is a term related to the Corona Virus Disease that has been firstly detected in 2019. It severely spread all over the world and the very first case had been obtained in Wuhan, China at the middle of the December 2019. WHO (World Health Organization) alerts it throughout the world and declare it as a pandemic disease and need to cure by taking various steps. It is essential to recognize the positive cases as exactly on schedule as could be required to prevent the further spread of this scourge and to quickly treat affected patients. The necessity for collaborator suggestive instruments has extended as there are no exact electronic device compartments available. Late disclosures got using radiology imaging techniques suggest that such image contain striking information about the COVID-19 contamination. This paper is intended to consequently identify the lungs impedances due to COVID-19 with undeniable degree of accuracy without tolerating the false one. This framework utilizes Laplacian based edge detection calculation and Morphological Dilation. Laplacian helps to obtain the magnitudinal structure of the lungs as well as the impairments and morphological dilation is a method through which missing edges can be dilated in the image that enhances the precision rate of the system. Laplacian is a second order derivatives that calculate edges in a single pass, it uses single kernel instead of two kernels as all other edges detection does.