Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
IEViT: An enhanced vision transformer architecture for chest X-ray image classification
98
Zitationen
3
Autoren
2022
Jahr
Abstract
Results showed that the proposed IEViT model outperformed all ViT's variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.
Ähnliche Arbeiten
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
2020 · 22.636 Zit.
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.284 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.279 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.658 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.284 Zit.