TY  - JOUR
A1  - Liang, Shuang
A1  - Liu, Huixiang
A1  - Gu, Yu
A1  - Guo, Xiuhua
A1  - Li, Hongjun
A1  - Li, Li
A1  - Wu, Zhiyuan
A1  - Liu, Mengyang
A1  - Tao, Lixin
T1  - Fast automated detection of COVID-19 from medical images using convolutional neural networks
T2  - Communications biology
N2  - Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.
KW  - Computational biology and bioinformatics
KW  - Diseases
KW  - Image processing
KW  - Infectious diseases
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69283
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-692834
SN  - 2399-3642
N1  - We would like to thank the Ministry of Science and Technology of the People’s Republic of China (Grant No. 2017YFB1400100) and the National Natural Science Foundation of China (Grant No. 61876059) for their support.
N1  - Code availability

We used standard software packages as described in the “Methods” section. The implementation details of the proposed framework can be downloaded from https://github.com/SHERLOCKLS/Detection-of-COVID-19-from-medical-images.
N1  - Data availability

The data sets used in this study (named Hybrid Datasets) are composed of public data sets from four public data repositories and a hospital data set provided by the cooperative hospital (Beijing Youan hospital). The four public data repositories are Covid-ChestXray-Dataset (CCD), Rsna-pneumonia-detection-challenge (RSNA), Lung Nodule Analysis 2016 (LUNA16), and Images of COVID-19 positive and negative pneumonia patients (ICNP), respectively. Full data of the Hybrid Data sets are available at Figshare (https://doi.org/10.6084/m9.figshare.13235009).
VL  - 4.2021
IS  - art. 35
SP  - 1
EP  - 13
PB  - Springer Nature
CY  - London
ER  -