Diagnosis of Lung segmentation for Chest X Ray images using XGBoost

Abstract

Beta version of the corona virus with severe acute respiratory syndrome coronavirus 2 (sara-Cov-2) has been identified at many places. Last year virus causes lung involvement like pneumonia named as corona virus disease (COVID 19) with strange etiology. Since this was e quite new virus no further tested drug is available due to the disease nodality there was nothing much available for patients' line of treatment. For curing the such unknown viral disease only possible solutions if to break the chain of infections. BY social distancing and test on the larger cluster can only be the hit method to cure such chains. Tomography in radiology is the diagnostic way to identify the disease at earlier stage. Clinical decisions can made on the basis of the CT and Chest X-ray images are the good means of identification of the corona virus. X - Ray scanning is economically available at most of the places even at remote areas so that general propel can also be benefitted by the early detection of the disease. People are acquiring a tool for the disease detection. In this paper report on investigation study on the all-possible means for cause of corona virus. In this paper we have proposed an automatic method for novel corona virus diagnosis by Chest X- ray images are proposed here. Proposed model XGBoost is based on the convolutions layered fashion deeply. By this proposed model we got the 90 percent accuracy of the testing data. Analysis is done on the comparative study of the XGBoost results and the already existing models results. By comparison we got the proposed method produces the better results as earlier models. Thus, proposed method can be used as effective means for the diagnosis of the corona virus.