Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images.

ABSTRACT: Novel Coronavirus is deadly for humans and animals. The ease of its dispersion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furtherm

SUBMITTER: Ahmad F 

PROVIDER: S-EPMC8058056 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature