A Survey of COVID-19 Detection From Chest X-Rays Using Deep Learning Methods

被引：0

作者：

Dornadula, Bhargavinath ^{[1
]}

Geetha, S. ^{[1
]}

Anbarasi, L. Jani ^{[1
]}

Kadry, Seifedine ^{[2
,3
,4
]}

机构：

[1] Vellore Inst Technol, Chennai, Tamil Nadu, India

[2] Noroff Univ Coll, Dept Appl Data Sci, Kristiansand, Norway

[3] Ajman Univ, Coll Engn & Informat Technol, Artificial Intelligence Res Ctr AIRC, Ajman, U Arab Emirates

[4] Lebanese Amer Univ, Dept Elect & Comp Engn, Byblos, Lebanon

来源：

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING | 2022年 / 18卷 / 01期

关键词：

Chest X-Rays; COVID-19; Detection; Deep Learning; Survey; Transfer Learning;

D O I：

10.4018/IJDWM.314155

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The coronavirus (COVID-19) outbreak has opened an alarming situation for the whole world and has been marked as one of the most severe and acute medical conditions in the last hundred years. Various medical imaging modalities including computer tomography (CT) and chest x-rays are employed for diagnosis. This paper presents an overview of the recently developed COVID-19 detection systems from chest x-ray images using deep learning approaches. This review explores and analyses the data sets, feature engineering techniques, image pre-processing methods, and experimental results of various works carried out in the literature. It also highlights the transfer learning techniques and different performance metrics used by researchers in this field. This information is helpful to point out the future research direction in the domain of automatic diagnosis of COVID-19 using deep learning techniques.

引用

页数：16

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