  The main objective of this work is to utilize state-of-the-art deep learning
approaches for the identification of pulmonary embolism in CTPA-Scans for
COVID-19 patients, provide an initial assessment of their performance and,
ultimately, provide a fast-track prototype solution (system). We adopted and
assessed some of the most popular convolutional neural network architectures
through transfer learning approaches, to strive to combine good model accuracy
with fast training. Additionally, we exploited one of the most popular
one-stage object detection models for the localization (through object
detection) of the pulmonary embolism regions-of-interests. The models of both
approaches are trained on an original CTPA-Scan dataset, where we annotated of
673 CTPA-Scan images with 1,465 bounding boxes in total, highlighting pulmonary
embolism regions-of-interests. We provide a brief assessment of some
state-of-the-art image classification models by achieving validation accuracies
of 91% in pulmonary embolism classification. Additionally, we achieved a
precision of about 68% on average in the object detection model for the
pulmonary embolism localization under 50% IoU threshold. For both approaches,
we provide the entire training pipelines for future studies (step by step
processes through source code). In this study, we present some of the most
accurate and fast deep learning models for pulmonary embolism identification in
CTPA-Scans images, through classification and localization (object detection)
approaches for patients infected by COVID-19. We provide a fast-track solution
(system) for the research community of the area, which combines both
classification and object detection models for improving the precision of
identifying pulmonary embolisms.
