A Review of COVID-19 Diagnostic Approaches in Computer Vision

被引：1
|
作者：
Zalluhoglu, Cemil [1 ]
机构：
[1] Hacettepe Univ, Dept Comp Engn, Ankara, Turkiye
来源：
CURRENT MEDICAL IMAGING | 2023年 / 19卷 / 07期
关键词：
COVID-19; computer vision; deep learning; machine learning; CT; X-Ray; LUS; CONVOLUTIONAL NEURAL-NETWORK; CORONAVIRUS DISEASE COVID-19; LUNG ULTRASOUND; PLEURAL LINE; X-RAY; CLASSIFICATION; CT; LOCALIZATION; PNEUMONIA; MARKERS;
D O I：
10.2174/1573405619666221222161832
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
Computer vision has proven that it can solve many problems in the field of health in recent years. Processing the data obtained from the patients provided benefits in both disease detection and follow-up and control mechanisms. Studies on the use of computer vision for COVID-19, which is one of the biggest global health problems of the past years, are increasing daily. This study includes a preliminary review of COVID-19 computer vision research conducted in recent years. This review aims to help researchers who want to work in this field.
引用
收藏
页码：695 / 712
页数：18
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