COVID-19 Identification from Chest X-Rays

被引：0

作者：

Mporas, Iosif ^{[1
]}

Naronglerdrit, Prasitthichai ^{[2
]}

机构：

[1] Univ Hertfordshire, Sch Phys Engn & Comp Sci, Hatfield AL10 9AB, Herts, England

[2] Kasetsart Univ, Fac Engn Sriracha, Dept Comp Engn, Sriracha Campus, Chon Buri, Thailand

来源：

PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON BIOMEDICAL INNOVATIONS AND APPLICATIONS (BIA 2020) | 2020年

关键词：

COVID-19; X-rays; transfer learning; convolutional neural networks;

D O I：

10.1109/bia50171.2020.9244509

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Artificial Intelligence and Data Science community has contributed to the global response against the new coronavirus, COVID-19. Significant attention has been given to detection and diagnosis tools with rapid diagnostic tools based on X-rays using deep learning being proposed. In this paper we present an evaluation of several well-known pretrained deep CNN models in a transfer learning setup for COVID-19 detection from chest X-ray images. Two different publicly available datasets were employed and different setups were tested using each of them separately of mixing them. The best performing models among the evaluated ones were the DenseNet, ResNet and Xception models, with the results indicating the possibility of identifying COVID-19 positive cases from chest X-ray images.

引用

页码：69 / 72

页数：4

共 29 条

[21] Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine [J].

Sethy, Prabira Kumar ;

Behera, Santi Kumari ;

Ratha, Pradyumna Kumar ;

Biswas, Preesat .

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES, 2020, 5 (04) :643-651

[22]

Simonyan K, 2015, Arxiv, DOI arXiv:1409.1556

[23] World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19) [J].

Sohrabi, Catrin ;

Alsafi, Zaid ;

O'Neill, Niamh ;

Khan, Mehdi ;

Kerwan, Ahmed ;

Al-Jabir, Ahmed ;

Iosifidis, Christos ;

Agha, Riaz .

INTERNATIONAL JOURNAL OF SURGERY, 2020, 76 :71-76

[24]

Szegedy C, 2017, AAAI CONF ARTIF INTE, P4278

[25] Rethinking the Inception Architecture for Computer Vision [J].

Szegedy, Christian ;

Vanhoucke, Vincent ;

Ioffe, Sergey ;

Shlens, Jon ;

Wojna, Zbigniew .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :2818-2826

[26] Laboratory Diagnosis of COVID-19: Current Issues and Challenges [J].

Tang, Yi-Wei ;

Schmitz, Jonathan E. ;

Persing, David H. ;

Stratton, Charles W. .

JOURNAL OF CLINICAL MICROBIOLOGY, 2020, 58 (06)

[27] Family violence and COVID-19: Increased vulnerability and reduced options for support [J].

Usher, Kim ;

Bhullar, Navjot ;

Durkin, Joanne ;

Gyamfi, Naomi ;

Jackson, Debra .

INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING, 2020, 29 (04) :549-552

[28] COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images [J].

Wang, Linda ;

Lin, Zhong Qiu ;

Wong, Alexander .

SCIENTIFIC REPORTS, 2020, 10 (01)

[29] Learning Transferable Architectures for Scalable Image Recognition [J].

Zoph, Barret ;

Vasudevan, Vijay ;

Shlens, Jonathon ;

Le, Quoc V. .

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :8697-8710

← 1 2 3 →