Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning
2
Zitationen
3
Autoren
2021
Jahr
Abstract
Through the development of the COVID-19 disease, various diagnosis methods have been studied. One of them is the computed tomography (CT), which has the best level of detail among medical image exams. The CT generates a repeatable and tiring workload, in addition to needing a team that is familiar with the findings that indicate pneumonia caused by COVID-19. To reduce this manual work and collaborate with these teams, several studies have been carried out using deep learning techniques. In this way, this study presents a review of the literature regarding the detection of COVID-19 in CT that uses deep learning to collaborate with a theoretical basis for future works.
Ähnliche Arbeiten
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
2020 · 22.617 Zit.
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.271 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.265 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.562 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.176 Zit.