scholarly journals Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7116
Author(s):  
Lucas O. Teixeira ◽  
Rodolfo M. Pereira ◽  
Diego Bertolini ◽  
Luiz S. Oliveira ◽  
Loris Nanni ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Image Database ◽  
Dice Coefficient ◽  
Lung Segmentation ◽  
X Ray ◽  
Explainable Artificial Intelligence ◽  
Segmented Images ◽  
Chest X Ray ◽  
The Impact ◽  
Different Sources

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.

scholarly journals Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results

2021 ◽  
Author(s):  
Matheus A. Renzo ◽  
Natália Fernandez ◽  
André A. Baceti ◽  
Natanael Nunes Moura Junior ◽  
André Anjos
Keyword(s):  
Semantic Segmentation ◽  
X Rays ◽  
Computer Aided Detection ◽  
Segmentation Evaluation ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Digitized Images ◽  
Public Datasets ◽  
The Impact

Analog X-Ray radiography is still used in many underdeveloped regions around the world. To allow these populations to benefit from advances in automatic computer-aided detection (CAD) systems, X-Ray films must be digitized. Unfortunately, this procedure may introduce artefacts which may severely impair the performance of such systems. This work investigates the impact digitized images may cause to deep neural networks trained for lung (semantic) segmentation on digital x-ray samples. While three public datasets for lung segmentation evaluation exist for digital samples, none are available for digitized data. To this end, a U-Net architecture was trained on publicly available data, and used to predict lung segmentation on a newly annotated set of digitized images. Our results show that the model is capable to effectively identify lung segmentation at digital X-Rays with a high intra-dataset (PR AUC: 0.99) and cross-dataset (PR AUC: 0.99) performances on unseen test data. When challenged against analog imaged films, the performance is substantially degraded (PR AUC: 0.90). Our analysis further suggests that the use of maximum F1 and precision-recall AUC (PR AUC) measures are not informative to identify segmentation problems in images.

scholarly journals Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1-11
Author(s):  
Alan Zhang
Keyword(s):  
Neural Networks ◽  
Learning Community ◽  
World Wide ◽  
Lung Region ◽  
Dice Coefficient ◽  
Training Set ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Validation Set

COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.

The Impact of Routine Admission Chest X-Ray Films on Patient Care

1985 ◽  
Vol 20 (6) ◽  
pp. 644-646 ◽  
Author(s):  
F. Allan Hubbell ◽  
Sheldon Greenfield ◽  
Judy L. Tyler ◽  
Kota Chetty ◽  
Frederic A. Wyle
Keyword(s):  
Patient Care ◽  
X Ray ◽  
Chest X Ray ◽  
The Impact

Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario

2021 ◽  
Author(s):  
Fabio K. H. De Barros ◽  
Andre L. Jeller Selleti ◽  
Vinicius A. P. Queiroz ◽  
Rodolfo M. Pereira ◽  
Carlos N. Silla
Keyword(s):  
Hierarchical Classification ◽  
X Ray ◽  
Chest X Ray ◽  
The Impact

Diagnosis of Lung segmentation for Chest X Ray images using XGBoost

2021 ◽  
Author(s):  
Preeti Arora ◽  
Saksham Gera ◽  
Vinod M Kapse
Keyword(s):  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 669
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Talha Anwar ◽  
Hind S. Alsaif ◽  
Sara Mhd. Bachar Chrouf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Disease Diagnosis ◽  
University Hospital ◽  
X Ray ◽  
Clinical Patient ◽  
Proposed Model ◽  
Chest X Ray ◽  
The Impact ◽  
Deep Learning Model

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

scholarly journals An Explainable Artificial Intelligence based Prospective Framework for COVID-19 Risk Prediction

2021 ◽  
Author(s):  
Vishal Sharma ◽  
Piyush   ◽  
Samarth Chhatwal ◽  
Bipin Singh
Keyword(s):  
Artificial Intelligence ◽  
Risk Prediction ◽  
Infection Risk ◽  
Alternative Methods ◽  
Confirmatory Test ◽  
Rt Pcr ◽  
Web Based ◽  
X Ray ◽  
Explainable Artificial Intelligence ◽  
Chest X Ray

Given the spread of COVID-19 to vast geographical regions and populations, it is not feasible to undergo or recommend the RT-PCR based tests to all individuals with flu-like symptoms. The reach of RT-PCR based testing is still limited due to the high cost of the test and huge population in few countries. Thus, alternative methods for COVID-19 infection risk prediction can be useful. We built an explainable artificial intelligence (AI) based integrated web-based prospective framework for COVID-19 risk prediction. We employed a two-step procedure for the non-clinical prediction of COVID19 infection risk. In the first step we assess the initial risk of COVID19 infection based on carefully selected parameters associated with COVID-19 positive symptoms from recent research. Generally, X-ray scans are cheaper and easily available in most government and private health centres. Therefore, based on the outcome of the computed initial risk in first step, we further provide an optional prediction using the chest X-ray scans in the second step of our proposed AI based prospective framework. Since there is a bottleneck to undergo an expensive RT-PCR based confirmatory test in economically backward nations, this is a crucial part of our explainable AI based prospective framework. The initial risk assessment outcome is analysed in combination with the advanced deep learning-based analysis of chest X-ray scans to provide an accurate prediction of COVID-19 infection risk. This prospective web-based AI framework can be employed in limited resource settings after clinical validation in future. The cost and time associated with the adoption of this prospective AI based prospective framework will be minimal and hence it will be beneficial to majority of the population living in low-income settings such as small towns and rural areas that have limited access to advanced healthcare facilities.

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks

2019 ◽  
Vol 177 ◽  
pp. 285-296 ◽  
Author(s):  
Johnatan Carvalho Souza ◽  
João Otávio Bandeira Diniz ◽  
Jonnison Lima Ferreira ◽  
Giovanni Lucca França da Silva ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Method ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray
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