Deep learning and traditional-based CAD schemes for the pulmonary
  embolism diagnosis: A survey
Seyed Hesamoddin Hosseini Amir Hossein Taherinia Mahdi Saadatmand
Nowadays, pulmonary Computed Tomography Angiography (CTA) is the main tool
for detecting Pulmonary Embolism (PE). However, manual interpretation of CTA
volume requires a radiologist, which is time-consuming and error-prone due to
the specific conditions of lung tissue, large volume of data, lack of
experience, and eye fatigue. Therefore, Computer-Aided Design (CAD) systems are
used as a second opinion for the diagnosis of PE. The purpose of this article
is to review, evaluate, and compare the performance of deep learning and
traditional-based CAD system for diagnosis PE and to help physicians and
researchers in this field. In this study, all articles available in databases
such as IEEE, ScienceDirect, Wiley, Springer, Nature, and Wolters Kluwer in the
field of PE diagnosis were examined using traditional and deep learning
methods. From 2002 to 2023, 23 papers were studied to extract the articles with
the considered limitations. Each paper presents an automatic PE detection
system that we evaluate using criteria such as sensitivity, False Positives
(FP), and the number of datasets. This research work includes recent studies,
state-of-the-art research works, and a more comprehensive overview compared to
previously published review articles in this research area.