PaperlayerView exact revision on arXiv

Source-linked AI summary

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

Karim Hammoudi, Halim Benhabiles, Mahmoud Melkemi, Fadi Dornaika, Ignacio Arganda-Carreras, Dominique Collard, Arnaud Scherpereel

arXiv:2004.03399v1eess.IVcs.CVcs.LG

TL;DR

The paper addresses limited specificity and access to radiographic evidence for COVID-19 screening by developing deep-learning models and health indicators from publicly available chest X-ray datasets. The models detected pneumonia and viral cases, while the indicators estimated infection status and patient risk in simulated scenarios.

Abstract

from arXiv · show

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.

I. INTRODUCTION

The paper frames COVID-19 screening as difficult because testing constraints and nonspecific imaging findings limit practical diagnosis. It proposes deep-learning analysis of publicly available chest X-ray data to detect pneumonia and support clinical evaluation.

Motivation

  • COVID-19 screening is arduous because RT-PCR kits may be unavailable and false negatives can contribute to disease propagation.The paper notes that deep CT analysis may predict some true positives when RT-PCR is negative.Source: §I · p. 1
  • Chest imaging findings are nonspecific and overlap with influenza, H1N1, SARS, and MERS.Chest X-rays may also have poor quality because examinations are often performed with patients lying or sitting for hygienic reasons.Source: §I · p. 1
  • Publicly open COVID-19-related CT datasets are limited, while some open chest X-ray datasets are available for deep-learning experiments.The paper presents this availability contrast as a motivation for investigating chest X-ray analysis.Source: §I · p. 2

Contributions

  • The paper proposes tailored deep-learning models for detecting pneumonia, especially viral cases, and easy-to-apply indicators for infection and patient-status evaluation.The stated aim is to support radiologists and clinicians, not replace medical advice.Source: §I · p. 2

Paper organization

  • The study experiments with training datasets, image-detection architectures, and analysis strategies, followed by performance evaluation and conclusion.The paper structure assigns experiments to Section 4 and the conclusion to Section 5.Source: §I · p. 2

II. RELATED WORK

Related work covers deep-learning approaches for COVID-19 analysis using CT and chest radiography. It positions chest X-rays as widely available resources investigated for upstream screening despite infections being more directly visible in CT images.

CT-based approaches

  • Prior studies used deep learning to screen COVID-19 from CT images and to segment and quantify infection in COVID-19 patient scans.The cited studies include a three-group CT classifier and a human-in-the-loop segmentation strategy.Source: §II · p. 2

Chest radiography

  • Pulmonary infections can be more directly visible in CT than chest X-rays, but chest X-rays remain relevant because they are widespread and often analyzed upstream of CT scans.Source: §II · p. 2
  • Prior work also proposed COVID-Net for detecting COVID-19 cases from chest radiography images using open datasets.The derived COVIDx dataset combined 5,941 posteroanterior radiographs from 2,839 patient cases.Source: §II · p. 2

Screening workflow

  • The paper's workflow links automatic infection detection in chest X-rays with COVID-19 screening, treating detected viral pneumonia as presumptive evidence during an epidemic.Source: Figure 1 · p. 3

A. CNN-based detection and evaluation of infected patients

The paper treats COVID-19 as a viral disease and uses three-way chest X-ray classification to identify normal, bacterial, or viral pneumonia cases for epidemic-context screening.

Detection assumption

1) Tailored CNN models:

The study builds CNN-based classifiers and a dual-use Inception-ResNetV2–RNN model that combines global feature extraction with local image-block predictions.

Tailored CNN models

  • Tailored CNN models classify normal, viral-pneumonia, and bacterial cases by outputting a predicted probability for each category.The models use ResNet34, ResNet50, or DenseNet169 backbones with a fully connected classifier head.Source: 1) Tailored CNN models · p. 3
  • A dual-use model combines a fine-tuned Inception-ResNetV2 feature extractor with a bidirectional LSTM for final image-block categorization.Source: 1) Tailored CNN models · p. 3

Local image analysis

  • The dual-use model divides each chest X-ray into grid cells and analyzes the resulting sequence of image blocks for local predictions.This second-level analysis is intended to retain image details that resizing may lose.Source: 1) Tailored CNN models · p. 3
  • Local predictions form a contamination matrix that supports estimation of health indicators such as a CNN-based infection ratio.The grid discretization is tuned according to the predictive performance of the architecture.Source: 1) Tailored CNN models · p. 3

Test data

  • Figure 3 provides chest X-ray samples from the test datasets.Source: Figure 3 · p. 3

2) Data preparation and model inputs:

The study reorganizes a public pediatric chest X-ray dataset into normal, bacterial pneumonia, and viral pneumonia classes, then prepares standardized inputs and a COVID-19 blind test set for model evaluation.

B. Estimation of CNN-based health indicators

The paper proposes health indicators that combine CNN-derived infection information with age and comorbidity risks to estimate patient status, while allowing temporal disease progression to be incorporated.

  • The measure F adds age, infection-rate, and comorbidity penalty scores, then divides their sum by a critical threshold T.Beyond T, the paper states that recovery is not possible.Source: B. Estimation of CNN-based health… · p. 4
  • The scoring system must be adjusted by health professionals to match reality.Source: B. Estimation of CNN-based health… · p. 4
  • The infection-related score S2 can use CNN class probability or a refined sub-image infection rate.The refined formulation divides an image into n = 9 sub-images and assigns S2 = (100/n) × N, where N is the number of virus-infected sub-images.Source: B. Estimation of CNN-based health… · p. 4
  • Each serious comorbidity receives a penalty of 100, while each moderated disease receives a penalty of 10 in the example.Examples of serious diseases include complicated hypertension, coronary artery disease, dialysis-dependent renal failure, and cancer under treatment.Source: B. Estimation of CNN-based health… · p. 4
  • The example sets the critical threshold T to 200, based on an assumed patient over 80 with a serious additional disease.The paper states that F ≥1 means the hope of escaping fatality is null, while values from 0 to 1 indicate progressively less hope of recovery.Source: B. Estimation of CNN-based health… · pp. 4–5
  • The measure can incorporate disease progression by comparing infection rates at two time points and applying aggravation, stability, or remission adjustments.The formulation uses t1 and t2, a stability threshold δ = 20, and bonus or malus terms tied to f(t2).Source: B. Estimation of CNN-based health… · p. 5

A. Performance of tailored CNN models

Tailored CNN and RNN models showed strong pneumonia detection, including on heterogeneous COVID-19 X-ray images, while image timing and age distributions support early and adult-case applicability.

CNN classification

  • 95.72% average classification accuracy was achieved by DenseNet169 on the Chest X-Ray Images (Pneumonia) dataset.Class accuracies were 97.97% for bacterial, 96.62% for virus, and 92.57% for normal cases.Source: A. Performance of tailored CNN models · p. 5

COVID-19 test performance

  • 99.3% of pneumonia cases were detected by the RNN architecture on the blind COVID-19 test set using default settings.The same architecture detected viral infection in 60.64% of cases with majority voting in sequences.Source: A. Performance of tailored CNN models · p. 5
  • The models produced robust pneumonia detection and satisfying viral detection despite radiography images originating from diverse sources.The extracted COVID-19 images came from at least 24 hospitals worldwide.Source: A. Performance of tailored CNN models · p. 5

Clinical applicability

  • Because many images were acquired during the first week of symptoms or hospitalization, the models may support early-stage detection when pneumonia is present.The passage states that quasi-totality of pneumonia cases were detected.Source: A. Performance of tailored CNN models · p. 5
  • Models trained mainly on 5,232 pediatric chest X-ray images may remain relevant for adult pneumonia detection across ages 20 to 80.The conclusion is based on the observed age distribution and near-complete pneumonia detection.Source: A. Performance of tailored CNN models · p. 5

B. Projection with the CNN-based health indicators

The paper reports infection-rate estimates from successive X-ray images and evaluates an additional indicator using synthetic patient data.

Health indicators

  • RNN-derived infection rates S2 were estimated from real pairs of successive X-ray images for five COVID-19-infected patients.The paper also gives examples of F calculated for nine patients from synthetic data.Source: B. Projection with the CNN-based… · p. 5

V. CONCLUSION

The study found promising performance from tailored deep learning models for pneumonia detection and explored health indicators for infection and aggravation assessment. It also identifies further validation and data needs before broader COVID-19 discrimination.

Performance

  • All tailored models exceeded 84% average accuracy for pneumonia detection on the reorganized Pneumonia dataset.Inception-ResNetV2 produced the minimum false-negative rate, 0.7%, on the blind test set.Source: §V · p. 5

Generalization

  • Transfer learning from pediatric chest X-ray training to adult infection screening was shown to be efficient in the experiments.Source: §V · p. 5

Health indicators

  • The study proposed easy-to-apply indicators for evaluating infection rate and aggravation risk in COVID-19 pneumonia scenarios.Source: §V · p. 5

Scope and future work

  • Future work may distinguish COVID-19 viral pneumonia from non-COVID-19 viral pneumonia when sufficient COVID-19 chest X-ray images become available.The authors state that model reliability must also be cross-checked with RT-PCR and clinical tests before deployment.Source: §V · p. 5

Related papers

Loading 2004.03399v1…