Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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Early Detection of Pneumonia from Chest X-Ray Images Using Deep Learning Approach

Smart Computing Techniques and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-1502-3_59 ◽

2021 ◽

pp. 595-604

Author(s):

Prateek Sarangi ◽

Pradosh Priyadarshan ◽

Swagatika Mishra ◽

Adyasha Rath ◽

Ganapati Panda

Keyword(s):

Deep Learning ◽

Early Detection ◽

Learning Approach ◽

X Ray ◽

Chest X Ray

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Risk group chest X-ray for the early detection of lung cancer

Orvosi Hetilap ◽

10.1556/oh.2008.28351 ◽

2008 ◽

Vol 149 (21) ◽

pp. 975-982 ◽

Cited By ~ 1

Author(s):

Gábor Kovács

Keyword(s):

Lung Cancer ◽

Early Detection ◽

Risk Group ◽

X Ray ◽

Chest X Ray

Magyarországon a gümőkór elleni szervezett küzdelem egyik legfontosabb eleme a mellkasi röntgenvizsgálaton alapuló évenkénti kötelező lakosságszűrés bevezetése volt. A hetvenes években megközelítően a teljes felnőttnépesség részt vett tüdőszűrésen. A tbc epidemiológiai helyzetének javulásával egyre nagyobb jelentőséggel bír a tüdőrákos betegeknek a szűrővizsgálat „melléktermékeként” történő korai felfedezése. A jövőben a kiemelési hatékonyság érdekében a súlyt a rizikócsoport vizsgálatára kell helyeznünk. Ez a tüdőrák esetében a negyvenévesnél idősebb dohányosok évenkénti ajánlott mellkasi röntgenvizsgálatát jelentheti. Az ajánlott vizsgálat előnyeinek bizonyítására elvégeztük a budapesti tüdőrák-prevalencia retrospektív elemzését. 2511 beteg adatai alapján megállapítottuk, hogy az öt évnél tovább élő tüdőrákos betegek 60%-át fedezték fel szűrővizsgálattal, szemben a panasszal felfedezettek 40%-ával. A szűréssel kiemeltek körében 34% volt az operábilitási ráta, míg a panasszal felfedezetteknek csak a 14%-át lehetett megoperálni. Megállapítottuk, hogy a radikális reszekciós műtét elvégzése valódi többlettúlélési esélyt jelent e betegek számára. Ezt az operált betegek kedvezőbb – három-négy éves – túlélése igazolja. Igaz ugyanakkor az is, hogy a későbbiekben, a kialakuló recidívák miatt a tüdőrák mortalitása nem javul a céllakosság körében. A jelenlegi kiemelési adatok ismeretében ez évente 5–600 beteg számára jelent reálisan többlettúlélési esélyt. Az ajánlott rizikócsoportok (>40 év, dohányosok) mellkasi röntgenvizsgálata esetén az operábilitási ráta további javulásával 1100–1200 beteg kaphatná meg ezt az esélyt.

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Computer Aided Tuberculosis Detection, A Review

10.21467/proceedings.114.20 ◽

2021 ◽

Author(s):

Pragya Shukla ◽

Jasleen Saini ◽

B.S. Saini

Keyword(s):

Probabilistic Methods ◽

Computer Aided Detection ◽

Chronological Order ◽

Borderline Cases ◽

X Ray ◽

Computational Capability ◽

Computer Aided ◽

Chest X Ray ◽

The Moment ◽

Visualization Techniques

This paper aims at presenting a complete picture of advances till now in the field of computer-aided detection of Pulmonary Tuberculosis using Chest X-ray Images. Advances are analyzed in chronological order as they happen and are divided into three phases in which technology shifted into new paradigms. Study concludes that although techniques that use Machine learning based methods for segmentation and classification are prevailing for the moment in terms of flexibility for very particular feature extraction in borderline cases where probabilistic methods can be tweaked according to requirements and accuracy, Deep Convolutional Neural Network based technique will secure higher standings as the computational capability and dataset management improves. Finally, briefly an attempt at using visualization techniques for borderline cases is discussed.

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Development of Severity and Mortality Prediction Models for COVID-19 Patients at Emergency Department Including the Chest X-Ray

10.21203/rs.3.rs-88282/v1 ◽

2020 ◽

Author(s):

Pilar Calvillo Batllés ◽

Leonor Cerdá-Alberich ◽

Carles Fonfría-Esparcia ◽

Ainhoa Carreres-Ortega ◽

Carlos Francisco Muñoz-Núñez ◽

...

Keyword(s):

Emergency Department ◽

Intensive Care ◽

Lymphocyte Count ◽

Prediction Models ◽

Mortality Prediction ◽

X Ray ◽

Extent Score ◽

Mortality Prediction Models ◽

Chest X Ray ◽

D Dimer

Abstract Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters. Methods: All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for model selection.Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC=0.94 and AUC-PRC=0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC=0.97 and AUC-PRC=0.78. The addition of CXR CNN-based indices slightly improved the predictive metrics for mortality (AUC-ROC=0.97 and AUC-PRC=0.83).Conclusion: The developed and internally validated severity and mortality prediction models could be useful as triage tools for COVID-19 patients and they should be further validated at different ED.

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Public Covid-19 X-ray datasets and their impact on model bias - a systematic review of a significant problem

10.1101/2021.02.15.21251775 ◽

2021 ◽

Author(s):

Beatriz Garcia Santa Cruz ◽

Matías Nicolás Bossa ◽

Jan Sölter ◽

Andreas Dominik Husch

Keyword(s):

Systematic Review ◽

Prediction Models ◽

Risk Of Bias ◽

Systematic Evaluation ◽

Bias Assessment ◽

X Ray ◽

Inappropriate Use ◽

Chest X Ray ◽

Almost All ◽

Aided Diagnosis

ABSTRACTComputer-aided-diagnosis for COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. This study provides a systematic evaluation of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias.Only 5 out of 256 identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably almost all of the datasets utilised in 78 papers published in peer-reviewed journals, are not among these 5 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use.This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

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