scholarly journals COVID-19 Detection and Analysis Using Chest X-Ray and CT- Scan Images

2021 ◽  
Vol 9 (12) ◽  
pp. 1732-1740
Author(s):  
Dawood Ahmad Dar
Keyword(s):  
Imaging Techniques ◽  
Ct Scans ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
X Ray ◽  
Patient Status ◽  
Chest X Ray ◽  
Proper Diagnosis ◽  
Percent Success ◽  
Percent Accuracy

Abstract: COVID-19 seems to be the most devastating and lethal illness characterized by an unique coronavirus for the human body. Coronavirus, which is considered to have originated in Wuhan, China, and is responsible for a huge number of deaths, spread swiftly around the world in December 2019. Early discovery of COVID-19 by proper diagnosis, especially in situations with no evident symptoms, could reduce the death rate of patients. The primary diagnostic tools for this condition are chest Xrays and CT scans. COVID-19 may be detected using a machine vision technique from chest X-ray pictures and CT scans, according to this study.The model's performance was evaluated using generalised data throughout the testing step. According to recent studies gained using radiological imaging techniques, such images convey crucial data about the COVID-19 virus. This proposed approach, which makes use of modern artificial intelligence (AI) techniques, has shown to be effective in recognising COVID-19, and when combined with radiological imaging, can aid in the correct detection of this disease. The proposed approach was created in order to provide accurate assessments for COVID and non-COVID patients.The results demonstrate that VGG-16 is the best architecture for the reference dataset, with 98.87 percent accuracy in network evaluations and 95.91 percent success in patient status identification. Convolutional layers were developed, with distinct filtering applied to each layer. As a result, the VGG-16 design performed well in the classification of COVID-19 cases. Nevertheless, by modifying it or adding a preprocessing step on top of it, this architecture allows for significant gains. Our methodology can be used to help radiologists validate their first screenings and can also be used to screen patients quickly via the cloud.

scholarly journals Usefulness of Lung Ultrasound in Paediatric Respiratory Diseases

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1783
Author(s):  
Francesco Sansone ◽  
Marina Attanasi ◽  
Paola Di Filippo ◽  
Giuseppe Francesco Sferrazza Papa ◽  
Sabrina Di Pillo ◽  
...  
Keyword(s):  
Respiratory Diseases ◽  
Lung Ultrasound ◽  
Imaging Techniques ◽  
Point Of Care ◽  
Signs And Symptoms ◽  
Diagnostic Tools ◽  
X Ray ◽  
Chest X Ray ◽  
Ionizing Radiations

Respiratory infection diseases are among the major causes of morbidity and mortality in children. Diagnosis is focused on clinical presentation, yet signs and symptoms are not specific and there is a need for new non-radiating diagnostic tools. Among these, lung ultrasound (LUS) has recently been included in point-of-care protocols showing interesting results. In comparison to other imaging techniques, such as chest X-ray and computed tomography, ultrasonography does not use ionizing radiations. Therefore, it is particularly suitable for clinical follow-up of paediatric patients. LUS requires only 5–10 min and allows physicians to make quick decisions about the patient’s management. Nowadays, LUS has become an early diagnostic tool to detect pneumonia during the COVID-19 pandemic. In this narrative review, we show the most recent scientific literature about advantages and limits of LUS performance in children. Furthermore, we discuss the major paediatric indications separately, with a paragraph fully dedicated to COVID-19. Finally, we mention potential future perspectives about LUS application in paediatric respiratory diseases.

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 Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

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.

scholarly journals Automated Detection of COVID-19 using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. , 824-830
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Global Economy ◽  
Imaging Techniques ◽  
Automated Detection ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
Daily Lives ◽  
Learning Techniques ◽  
The World

Corona virus 2019 (COVID-2019), has first appeared in Wuhan, China in December 2019, spread around the world rapidly causing thousands of fatalities. It is caused a devastating result in our daily lives, public health, and also the global economy. It is important to sight the positive cases as early as possible therefore forestall any unfoldment of this epidemic and to quickly treat affected patients. The necessity for auxiliary diagnostic tools has increased as there aren't any accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Coupling deep learning techniques with radiological imaging may end up within the accurate detection of this disease. This assistance will help to beat the matter of an absence of specialized physicians in the remote villages.

scholarly journals Precise Prediction of COVID-19 in Chest X-Ray Images Using KE Sieve Algorithm

2020 ◽  
Author(s):  
S Sai Thejeshwar ◽  
Chaitanya Chokkareddy ◽  
K Eswaran
Keyword(s):  
Ct Scan ◽  
Network Architecture ◽  
Intelligent Systems ◽  
Imaging Techniques ◽  
Health Care Systems ◽  
Prediction Method ◽  
Lung Infection ◽  
Disease Diagnosis ◽  
X Ray ◽  
Chest X Ray

The novel coronavirus (COVID-19) pandemic is pressurizing the healthcare systems across the globe and few of them are on the verge of failing. The detection of this virus as early as possible will help in contaminating the spread of it as the virus is mutating itself as fast as possible and currently there are about 4,300 strains of the virus according to the reports. Clinical studies have shown that most of the COVID-19 patients suffer from a lung infection similar to influenza. So, it is possible to diagnose lung infection using imaging techniques. Although a chest computed tomography (CT) scan has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available across the hospitals due to its considerably lower cost and faster imaging time than CT scan. The advancements in the area of machine learning and pattern recognition has resulted in intelligent systems that analyze CT Scans or X-ray images and classify between pneumonia and normal patients. This paper proposes KE Sieve Neural Network architecture, which helps in the rapid diagnosis of COVID-19 using chest X-ray images. This architecture is achieving an accuracy of 98.49%. This noninvasive prediction method can assist the doctors in this pandemic and reduce the stress on health care systems.

scholarly journals The Role of Imaging In The Diagnosis of Pulmonary Embolism

2019 ◽  
Vol 2 (1) ◽  
pp. 57
Author(s):  
Alfian Nur Rosyid ◽  
M. Yamin ◽  
Arina Dery Puspitasari
Keyword(s):  
Risk Factors ◽  
Pulmonary Embolism ◽  
Gold Standard ◽  
Common Condition ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Proper Diagnosis ◽  
D Dimer

Pulmonary embolism is a common condition and sometimes can be life-threatening. A proper diagnosis can reduce mortality. Some examinations are needed to diagnose pulmonary embolism, including assessing the risk factors, clinical examination, D-dimer tests, and imaging. Imaging is necessary when the previous assessment requires further investigation. There are more imaging that can be used to diagnose and assess the severity of pulmonary embolism. However, it is still controversial regarding imaging modalities for optimizing pulmonary embolism diagnose. Chest X-Ray cannot exclude pulmonary embolism, but it is needed to guide the next examinations and to find alternative diagnoses. Pulmonary Multi-Detector CT Angiography is the gold standard to diagnose pulmonary embolism.

Bilateral spontaneous chylothorax presenting as a left-sided neck mass

2021 ◽  
Vol 14 (4) ◽  
pp. e240320
Author(s):  
Sarah Akbar ◽  
Rajeev Advani ◽  
Rohini Aggarwal
Keyword(s):  
Soft Tissues ◽  
Anterior Chest Wall ◽  
Neck Mass ◽  
Ct Scans ◽  
X Ray ◽  
Low Fat Diet ◽  
History Of ◽  
Chest X Ray ◽  
Inflammatory Changes

A previously well 36-year-old woman presented with a 2-day history of a tender left-sided neck mass associated with left-sided otalgia and odynophagia. On examination, there was a diffuse, tender swelling of the left anterior triangle of the neck with extension onto the anterior chest wall. Ultrasound and CT scans of the neck revealed extensive inflammatory changes in the soft tissues of the neck and a chest X-ray showed blunting of both costophrenic angles. A diagnostic pleural aspiration was subsequently performed and yielded chyle. The patient was commenced on total parenteral nutrition and placed on a low-fat diet; symptoms completely resolved within 5 days, and at follow-up at 6 weeks, there were no further episodes or complications. Bilateral spontaneous chylothorax is a rarely reported phenomenon in the literature; it has been reported in females following the possibility of minor physical exercise such as stretching or hyperextension of the neck.

scholarly journals The potential application of dual-energy subtraction radiography for COVID-19 pneumonia imaging

2021 ◽  
pp. 20201384
Author(s):  
Brent van der Heyden
Keyword(s):  
Soft Tissues ◽  
Imaging Techniques ◽  
Dual Energy ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Application ◽  
Chest X Ray ◽  
Energy Subtraction ◽  
Potential Use

X-ray imaging plays a crucial role in the confirmation of COVID-19 pneumonia. Chest X-ray radiography and CT are two major imaging techniques that are currently adopted in the diagnosis of COVID-19 pneumonia. However, dual-energy subtraction radiography is hardly discussed as potential COVID-19 imaging application. More advanced X-ray radiography equipment often supports dual-energy subtraction X-ray radiography. Dual-energy subtraction radiography enables the calculation of pseudo-radiographs, in which bones are removed and only soft-tissues are highlighted. In this commentary, the author would like to draw the attention to the potential use of dual-energy subtraction X-ray radiography (i.e. soft-tissue pseudo-radiography) for the assessment and the longitudinal follow-up of COVID-19 pneumonia.

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