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. 2021 Jun 8;45(7):75.
doi: 10.1007/s10916-021-01745-4.

Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Karim Hammoudi  1   2 , Halim Benhabiles  3 , Mahmoud Melkemi  4   5 , Fadi Dornaika  6   7 , Ignacio Arganda-Carreras  6   7   8 , Dominique Collard  9   10 , Arnaud Scherpereel  11
Affiliations

Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Karim Hammoudi et al. J Med Syst. .

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.

Keywords: COVID-19; Chest X-ray images (CXR); Coronavirus disease; Health scoring system; Pneumonia detection; Radiology.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Global workflow using deep learning for automatic detection of infection towards supporting COVID-19 screening from chest X-ray images. In a COVID-19 epidemic context, a detected viral pneumonia can particularly presume a COVID-19 infection
Fig. 2
Fig. 2
Global workflow using deep learning based for automatic estimation of a CNN-based infection rate indicator from chest X-ray images
Fig. 3
Fig. 3
Chest X-ray samples from the test datasets. Row 1 shows image categories. Row 2 shows various artifacts captured with chest X-rays such as writings (e.g.; letter “R”) and medical devices (e.g., tubes, sensors)
Fig. 4
Fig. 4
Histogram of classification accuracy obtained for each class by using varied architecture types with the Chest X-Ray Images (Pneumonia) dataset
Fig. 5
Fig. 5
Graph obtained by representing the distribution of F values for a patient aged between 60 and 69 considering the possible diseases and rates of infection given in Table 8
See this image and copyright information in PMC

References

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