A Novel Deep Learning Framework for Pulmonary Embolism Detection for Covid-19 Management

ABSTRACT

Pulmonary Embolism is a blood clot in the lung which restricts the blood flow and reduces blood oxygen level resulting in mortality if it is untreated. Further, pulmonary embolism is evidenced prominently in the segmental and subsegmental regions of the computed tomography angiography images in COVID-19 patients. Pulmonary embolism detection from these images is a significant research problem in the challenging COVID-19 pandemic in the venture of early disease detection, treatment, and prognosis. Inspired by several investigations based on deep learning in this context, a two-stage framework has been proposed for pulmonary embolism detection which is realized as a segmentation model. It is implemented as a cascade of convolutional superpixel neural network and a regularized UNet network for the segmentation of embolism candidates as well as embolisms, respectively. The proposed model has been tested with two public datasets and it has achieved a testing accuracy of 99%. The proposed model demonstrates high sensitivities of 88.43%, 88.36%, and 89.93% at 0, 2, and 5 mm localization errors, respectively for two false positives and they are superior to the state-of-the-art models, signifying potential applications in the treatment protocols of diverse pulmonary diseases and COVID-19.