A high‐speed unsupervised hardware architecture for rapid diagnosis of COVID‐19

ABSTRACT: Summary In the diagnosis of COVID‐19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time‐consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood‐count‐test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased

SUBMITTER: Ratnakumar R 

PROVIDER: S-EPMC9538980 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature