scholarly journals Detecting COVID-19 in Chest X-Ray Images via MCFF-Net

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Wang ◽  
Yutao Li ◽  
Ji Li ◽  
Peng Zhang ◽  
Xin Wang
Keyword(s):  
Feature Fusion ◽  
Rt Pcr ◽  
X Ray ◽  
Detection Technology ◽  
Rapid Spread ◽  
The World ◽  
Chest X Ray ◽  
Recognition Efficiency ◽  
Low Sensitivity ◽  
Sensitivity Specificity

COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits.

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

Should Chest X-ray Be Used in Diagnosing COVID-19?

2020 ◽  
Vol 54 ◽  
Author(s):  
Maria Cristina Z. San Jose ◽  
Valentin C. Dones
Keyword(s):  
British Society ◽  
Normal Lung ◽  
Rt Pcr ◽  
Thoracic Imaging ◽  
X Ray ◽  
Canadian Association ◽  
Lung Abnormalities ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Pcr Test

While chest x-ray is readily available and may precede RT-PCR test, chest x-ray has low sensitivity early in the COVID-19 disease and shows non-specific lung abnormalities in COVID-19 patients. Chest x-ray is part of the initial diagnostic tool used on COVID-19 patients in some hospitals as it yields fast results compared with reverse transcription-polymerase chain reaction (RT-PCR). Chest Computed Tomography (CT) has been reported to be more sensitive than chest x-ray in determining the presence of COVID-19. Chest x-ray findings in confirmed COVID-19 patients show:  Normal lung findings early in the illness and in mildly symptomatic patients Typical ground-glass opacities and consolidation in the lung periphery Lung abnormalities are non-specific and may likewise be present in other infections and coronavirus-types of pneumonia The American College of Radiology (ACR), Center for Disease Control and Prevention (CDC), Canadian Association of Radiologists (CAR), Canadian Society of Thoracic Radiology (CSTR), and British Society of Thoracic Imaging do not recommend the use of chest x-ray to diagnose COVID-19. The Fleisher Society, composed of radiologists and pulmonologists in ten countries, does not recommend a chest x-ray for patients suspected of mild COVID-19. A chest x-ray is recommended for patients with moderate to severe COVID-19 needing immediate triage and patients at high risk for disease progression. Despite presence of chest x-ray findings suggesting COVID-19, RT-PCR test remains the standard diagnostic procedure.

scholarly journals COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 315
Author(s):  
Soham Chattopadhyay ◽  
Arijit Dey ◽  
Pawan Kumar Singh ◽  
Zong Woo Geem ◽  
Ram Sarkar
Keyword(s):  
State Of The Art ◽  
Golden Ratio ◽  
World Health ◽  
Rt Pcr ◽  
X Ray ◽  
Global Pandemic ◽  
The World ◽  
Chest X Ray ◽  
Feature Selection Approach ◽  
Health Organization

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

scholarly journals D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Yiyang Hu ◽  
Yanhong Luo ◽  
Wei Wang
Keyword(s):  
Feature Fusion ◽  
Detection Accuracy ◽  
Data Sets ◽  
X Ray ◽  
Public Data ◽  
Chest X Ray ◽  
Feature Information ◽  
Novel Coronavirus ◽  
Sensitivity Specificity ◽  
Deep Learning Model

Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately.

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

scholarly journals Chest CT as a Complement to RT-PCR to Confirm and Follow-up COVID-19 Patients

Medicinus ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 31
Author(s):  
Aziza Ghanie Icksan ◽  
Muhammad Hafiz ◽  
Annisa Dian Harlivasari
Keyword(s):  
Chest Ct ◽  
Rt Pcr ◽  
High Resolution Computed Tomography ◽  
X Ray ◽  
First Case ◽  
Chest X Ray ◽  
Polymerase Chain

<p><strong>Background : </strong>The first case of COVID-19 in Indonesia was recorded in March 2020. Limitation of reverse-transcription polymerase chain reaction (RT-PCR) has put chest CT as an essential complementary tool in the diagnosis and follow up treatment for COVID-19. Literatures strongly suggested that High-Resolution Computed Tomography (HRCT) is essential in diagnosing typical symptoms of COVID-19 at the early phase of disease due to its superior sensitivity  (97%) compared to chest x-ray (CXR).</p><p>The two cases presented in this case study showed the crucial role of chest CT with HRCT to establish the working diagnosis and follow up COVID-19 patients as a complement to RT-PCR, currently deemed a gold standard.<strong></strong></p>

scholarly journals The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110162
Author(s):  
Fengxia Zeng ◽  
Yong Cai ◽  
Yi Guo ◽  
Weiguo Chen ◽  
Min Lin ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Diagnostic Value ◽  
Rapid Identification ◽  
Chest Ct ◽  
Ct Scans ◽  
Ground Glass ◽  
X Ray ◽  
Chest X Ray ◽  
Laboratory Examinations ◽  
Sensitivity Specificity

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients ( p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs ( p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

scholarly journals The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2206
Author(s):  
Dana Li ◽  
Lea Marie Pehrson ◽  
Carsten Ammitzbøl Lauridsen ◽  
Lea Tøttrup ◽  
Marco Fraccaro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Assessment Tool ◽  
Cochrane Library ◽  
Original Research ◽  
Quality Assessment Tool ◽  
Bias Assessment ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

scholarly journals 1184. Making Pneumonia Surveillance Easy: Automation of Pneumonia Case Detection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S424-S425
Author(s):  
Dan Ding ◽  
Anna Stachel ◽  
Eduardo Iturrate ◽  
Michael Phillips
Keyword(s):  
Language Processing ◽  
Cost Effective ◽  
The United States ◽  
Surveillance Data ◽  
X Rays ◽  
Healthcare Facilities ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity ◽  
Time Critical

Abstract Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

scholarly journals COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images

2020 ◽  
Vol 10 (16) ◽  
pp. 5683 ◽  
Author(s):  
Lourdes Duran-Lopez ◽  
Juan Pedro Dominguez-Morales ◽  
Jesús Corral-Jaime ◽  
Saturnino Vicente-Diaz ◽  
Alejandro Linares-Barranco
Keyword(s):  
Deep Learning ◽  
Imaging Techniques ◽  
Learning System ◽  
Screening Process ◽  
X Ray ◽  
Average Accuracy ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Control ◽  
Aided Diagnosis

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

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