The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.
A Cerenkov fiber-optic dosimeter (CFOD) is fabricated using plastic optical fibers to measure Cerenkov radiation induced by a therapeutic photon beam. We measured the Cerenkov radiation generated in optical fibers in various irradiation conditions to evaluate the usability of Cerenkov radiation for a photon beam therapy dosimetry. As a results, the spectral peak of Cerenkov radiation was measured at a wavelength of 515 nm, and the intensity of Cerenkov radiation increased linearly with increasing irradiated length of the optical fiber. Also, the intensity peak of Cerenkov radiation was measured in the irradiation angle range of 30 to 40 deg. In the results of Monte Carlo N-particle transport code simulations, the relationship between fluxes of electrons over Cerenkov threshold energy and energy deposition of a 6 MV photon beam had a nearly linear trend. Finally, percentage depth doses for the 6 MV photon beam could be obtained using the CFOD and the results were compared with those of an ionization chamber. Here, the mean dose difference was about 0.6%. It is anticipated that the novel and simple CFOD can be effectively used for measuring depth doses in radiotherapy dosimetry.
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