  The clinical symptoms of pulmonary embolism (PE) are very diverse and
non-specific, which makes it difficult to diagnose. In addition, pulmonary
embolism has multiple triggers and is one of the major causes of vascular
death. Therefore, if it can be detected and treated quickly, it can
significantly reduce the risk of death in hospitalized patients. In the
detection process, the cost of computed tomography pulmonary angiography (CTPA)
is high, and angiography requires the injection of contrast agents, which
increase the risk of damage to the patient. Therefore, this study will use a
deep learning approach to detect pulmonary embolism in all patients who take a
CT image of the chest using a convolutional neural network. With the proposed
pulmonary embolism detection system, we can detect the possibility of pulmonary
embolism at the same time as the patient's first CT image, and schedule the
CTPA test immediately, saving more than a week of CT image screening time and
providing timely diagnosis and treatment to the patient.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning