Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and accuracy of disease identification in CT images. Current techniques require images to be the same size and resolution in order to properly train DL algorithms. LLNL scientists have developed a technique which automatically samples across various views and backgrounds to pre-select possible objects of interest. This technique overcomes the limitations of current techniques and provides more efficient object identification, saving physicians time and potential patient lives.