Viral Pneumonia Cases Research Articles

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less.

The effect of Gaussian noise on pneumonia detection on chest radiographs, using convolutional neural networks

IntroductionChest X-rays (CXR) with under-exposure increase image noise and this may affect convolutional neural network (CNN) performance. This study aimed to train and validate CNNs for classifying pneumonia on CXR as normal or pneumonia acquired at different image noise levels. MethodsThe study used the curated and publicly available “Chest X-Ray Pneumonia” dataset of 5856 AP CXR classified into 1583 normal, 4273 viral and bacterial pneumonia cases. Gaussian noise with zero mean was added to the images, at 5 image noise variance levels, corresponding to decreasing exposure. Each noise-level dataset was split into 80% for training, 10% for validation, and 10% for test data and then classified using custom trained sequential CNN architecture. Six classification tasks were developed for five Gaussian noise levels and the original dataset. Sensitivity, specificity, predictive values and accuracy were used as evaluation performance metrics. ResultsCNN evaluation on the different datasets revealed no performance drop from the original dataset to the five datasets with different noise levels. Sensitivity, specificity and accuracy for the normal datasets were 98.7%, 76.1% and 90.2%. For the five Gaussian noise levels the sensitivity, specificity and accuracy ranged from 96.9% to 98.2%, 94.4%–98.7% and 96.8%–97.6%, respectively. A heat map was used for visual explanation of the CNNs. ConclusionThe CNNs sensitivity maintained, and the specificity increased in distinguishing between normal and pneumonia CXR with the introduction of image noise. Implications for practiceNo performance drops of CNNs in distinguishing cases with and without pneumonia CXR with different Gaussian noise levels was observed. This has potential for decreasing radiation dose to patients or maintaining exposure parameters for patients that require additional radiographs.

Anthropogenic Sewage Water Circuit as Vector for SARS-CoV-2 Viral ARN Transport and Public Health Assessment, Monitoring and Forecasting-Sibiu Metropolitan Area (Transylvania/Romania) Study Case.

Water is a risk factor for epidemics of waterborne diseases with effects on human health. In 2019, new viral pneumonia cases occurred in China and spread worldwide. The aim of this study was to assess the feasibility and accuracy of a wastewater-based epidemiological (WBE) monitoring tool in a SARS-CoV-2 hot spot (Sibiu City metropolitan area), namely to highlight the correlation between the number of infections on the days of sampling and the amount of viral RNA detected in wastewater. Wastewater samples were collected once a week, and viral RNA was extracted and quantified. In parallel, the daily number of SARS-CoV-2 infections was obtained from the local council. The correlation between the number of infections and viruses detected in sewage was measured by Pearson correlation coefficients. The results show the amount of viral RNA in the wastewater is directly correlated with the number of infections reported in the week up to the sampling day and also the number of infections reported for the sampling day. Moreover, correlation coefficients show the amount of viral RNA in wastewater increases in advance of the increase in reported infection cases. Therefore, WBE can be used as a tool for monitoring virus spread trends in human communities and can help anticipate the trend of this type of viral infection.

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss

Healthcare systems worldwide have been struggling since the beginning of the COVID-19 pandemic. The early diagnosis of this unprecedented infection has become their ultimate objective. Detecting positive patients from chest X-ray images is a quick and efficient solution for overloaded hospitals. Many studies based on deep learning (DL) techniques have shown high performance in classifying COVID-19 chest X-ray images. However, most of these studies suffer from a class imbalance problem mainly due to the limited number of COVID-19 samples. Such a problem may significantly reduce the efficiency of DL classifiers. In this work, we aim to build an accurate model that assists clinicians in the early diagnosis of COVID-19 using balanced data. To this end, we trained six state-of-the-art convolutional neural networks (CNNs) via transfer learning (TL) on three different COVID-19 datasets. The models were developed to perform a multi-classification task that distinguishes between COVID-19, normal, and viral pneumonia cases. To address the class imbalance issue, we first investigated the Weighted Categorical Loss (WCL) and then the Synthetic Minority Oversampling Technique (SMOTE) on each dataset separately. After a comparative study of the obtained results, we selected the model that achieved high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and AUC compared to other recent works. DenseNet201 and VGG-19 claimed the best scores. With an accuracy of 98.87%, an F1_Score of 98.21%, a sensitivity of 98.86%, a specificity of 99.43%, a precision of 100%, and an AUC of 99.15%, the WCL combined with CheXNet outperformed the other examined models.

Improved COVID-19 detection with chest x-ray images using deep learning.

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning.

Coronavirus disease (COVID‐19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID‐19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID‐19 can protect people from severe infection. Unfortunately, COVID‐19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID‐19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID‐19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID‐19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID‐19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre‐trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X‐rays (1200 cases of COVID‐19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X‐ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID‐19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1‐score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID‐19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.

Clinical characteristics of hospitalized term and preterm infants with community-acquired viral pneumonia

BackgroundPneumonia is a serious problem that threatens the health of newborns. This study aimed to investigate the clinical characteristics of hospitalized term and preterm infants with community-acquired viral pneumonia.MethodsThis was a retrospective analysis of cases of community-acquired viral pneumonia in the Neonatal Department. Nasopharyngeal aspirate (NPA) samples were collected for pathogen detection, and clinical data were collected. We analysed pathogenic species and clinical characteristics among these infants.ResultsRSV is the main virus in term infants, and parainfluenza virus (PIV) 3 is the main virus in preterm infants. Patients infected with PIV3 were more susceptible to coinfection with bacteria than those with respiratory syncytial virus (RSV) infection (p < 0.05). Preterm infants infected with PIV3 were more likely to be coinfected with bacteria than term infants (p < 0.05), mainly gram-negative bacteria (especially Klebsiella pneumonia). Term infants with bacterial infection were more prone to fever, cyanosis, moist rales, three concave signs, elevated C-reactive protein (CRP) levels, respiratory failure and the need for higher level of oxygen support and mechanical ventilation than those with simple viral infection (p < 0.05). The incidence of hyponatremia in neonatal community-acquired pneumonia (CAP) was high.ConclusionsRSV and PIV3 were the leading causes of neonatal viral CAP. PIV3 infection is the main cause of viral CAP in preterm infants, and these individuals are more likely to be coinfected with bacteria than term infants, mainly gram-negative bacteria. Term infants with CAP coinfected with bacteria were more likely to have greater disease severity than those with single viral infections.

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.

Influenza A, Influenza B, and SARS-CoV-2 Similarities and Differences - A Focus on Diagnosis.

In late December 2019, the first cases of viral pneumonia caused by an unidentified pathogen were reported in China. Two years later, SARS-CoV-2 was responsible for almost 450 million cases, claiming more than 6 million lives. The COVID-19 pandemic strained the limits of healthcare systems all across the world. Identifying viral RNA through real-time reverse transcription-polymerase chain reaction remains the gold standard in diagnosing SARS-CoV-2 infection. However, equipment cost, availability, and the need for trained personnel limited testing capacity. Through an unprecedented research effort, new diagnostic techniques such as rapid diagnostic testing, isothermal amplification techniques, and next-generation sequencing were developed, enabling accurate and accessible diagnosis. Influenza viruses are responsible for seasonal outbreaks infecting up to a quarter of the human population worldwide. Influenza and SARS-CoV-2 present with flu-like symptoms, making the differential diagnosis challenging solely on clinical presentation. Healthcare systems are likely to be faced with overlapping SARS-CoV-2 and Influenza outbreaks. This review aims to present the similarities and differences of both infections while focusing on the diagnosis. We discuss the clinical presentation of Influenza and SARS-CoV-2 and techniques available for diagnosis. Furthermore, we summarize available data regarding the multiplex diagnostic assay of both viral infections.

Specific course of COVID-19 associated pneumonia in the tuberculosis patient after pleuropneumonectomy

The article describes a clinical case of viral pneumonia associated with COVID-19 of the only lung, it developed in the patient 30 days after pleuropneumonectomy which was done to manage fibrous cavernous pulmonary tuberculosis. The favorable course of COVID-19 was due to its timely detection and initiation of treatment, and possibly, it was due to using anti-tuberculosis chemotherapy containing antibacterial drugs that prevented nosocomial infections.

Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images

The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.

Correlation Between Pneumonia and COVID-19

COVID-19 has recently been expanded to more than 100 countries and territories worldwide, resulting in widespread alarming. COVID-19 is distributed in batches, mainly through respiratory drops and close contact. Fever, fatigue, and dry cough are the most common symptoms, but they can be severe due to fatigue, sore throat, and headache. Symptoms include runny nose, runny nose, and diarrhea in a few cases. Acute Respiratory Distress Syndrome (ARDS) can develop rapidly if the condition is severe (ARDS). The gold standard for diagnosing COVID-19 is a reversal of the polymerase chain reaction (RT-PCR) and the next generation sequence (NGS). Most cases of adult viral pneumonia were caused by influenza viruses. Coronavirus disease 2019 (COVID-19) is now a dangerous global pandemic. The COVID-19 epidemic is projected to last for several months, coinciding with the influenza season. Conclusion: Mortality ratio and number of infections reported worldwide due to this COVID-19 virus is very high as compared to SARS or MERS. Coronavirus is extremely contagious and communicable, which can be spread among society via aerosols, saliva droplets, and close contact with infected person and surfaces. Self-isolation and quarantine are the initial and result oriented protocols which are required to be followed, along with paramedical observation to avoid future transmission of this disease. This disease has become very serious issue of concern in positively tested elderly patients and become warning signals around the globe.

Review of selected animal models for respiratory coronavirus infection and its application in drug research.

Numerous viral pneumonia cases have been reported in Wuhan, Hubei in December 2019. The pathogen has been identified as a novel coronavirus, which was named severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). The biological characteristics and pathogenesis mechanism of SARS‐CoV‐2 are unclear and under progress. At present, no specific preventive and therapeutic drugs are available. Animal models can reproduce the viral replication cycle and the significant functions of respiratory coronavirus infection and are urgently needed to evaluate the efficacy of drugs and vaccines, the transmission route of respiratory coronavirus, clinical features, and so on. We reviewed the current animal models of respiratory coronavirus (SARS‐CoV, MERS‐CoV, and SARS‐CoV‐2) infection and made a comparative analysis of the route of inoculation, virus replication, clinical signs, histopathology, application, advantages, and disadvantages. Animal models of respiratory coronavirus include susceptible animal models, genetically modified models, and various animal models of infected virus adaptation strains, such as nonhuman primates, mice, hamsters, ferrets, New Zealand rabbits, cats, and other animal models, all of which have distinct advantages and limitations. This review will provide relevant information and important insights for disease management and control.

An Integrated Approach in COVID-19 Patient with High Oxygen Dependency Status

Covid-19 has become a global pandemic leading towards thousands of death worldwide.It is infectious disease caused by a new corona virus called SARS-cov-2. WHO learned of the new virus on 31 december 2019 for the first time, following a report of cluster of cases of viral pneumonia in Wuhan, China. However, we report a presentation of covid infection in a 45 year old male in a covid hospital where he was treated with integrated approach i.e Allopathy,Ayurveda,TCM(Trad itional Chinese Medicine),Yoga and physiotherapy.During treatment Patient was kept on cpap for some days and got discharged after complete treatment.

Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images

Abstract Despite the great efforts to find an effective way for coronavirus disease 2019 (COVID-19) prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via chest X-ray images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional neural network (HQ-CNN) model using random quantum circuits as a base to detect COVID-19 patients with chest X-ray images is presented. A collection of 5445 chest X-ray images, including 1350 COVID-19, 1350 normal, 1345 viral pneumonia, and 1400 bacterial pneumonia images, were used to evaluate the HQ-CNN. The proposed HQ-CNN model has achieved higher performance with an accuracy of 98.6% and a recall of 99% on the first experiment (COVID-19 and normal cases). Besides, it obtained an accuracy of 98.2% and a recall of 99.5% on the second experiment (COVID-19 and viral pneumonia cases). Also, it obtained 98% and 98.8% for accuracy and recall, respectively, on the third dataset (COVID-19 and bacterial pneumonia cases). Lastly, it achieved accuracy and recall of 88.2% and 88.6%, respectively, on the multiclass dataset cases. Moreover, the HQ-CNN model is assessed with the statistical analysis (i.e. Cohen’s Kappa and Matthew correlation coefficients). The experimental results revealed that the proposed HQ-CNN model is able to predict the positive COVID-19 cases.

Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.

The COVID-19 diffused quickly throughout the world and converted as a pandemic. It has caused a destructive effect on both regular lives, common health and global business. It is crucial to identify positive patients as shortly as desirable to limit this epidemic’s further diffusion and to manage immediately affected cases. The demand for quick assistant distinguishing devices has developed. Recent findings achieved utilizing radiology imaging systems propose that such images include salient data about the COVID-19. The utilization of progressive artificial intelligence (AI) methods linked by radiological imaging can help the reliable diagnosis of COVID-19. As radiography images can recognize pneumonia infections, this research brings an accurate and automatic technique based on a deep residual network to analyze chest X-ray images to monitor COVID-19 and diagnose verified patients. The physician states that it is significantly challenging to separate COVID-19 from common viral and bacterial pneumonia, while COVID-19 is additionally a variety of viruses. The proposed network is expanded to perform detailed diagnostics for two multi-class classification (COVID-19, Normal, Viral Pneumonia) and (COVID-19, Normal, Viral Pneumonia, Bacterial Pneumonia) and binary classification. By comparing the proposed network with the popular methods on public databases, the results show that the proposed algorithm can provide an accuracy of 92.1% in classifying multi-classes of COVID-19, normal, viral pneumonia, and bacterial pneumonia cases. It can be applied to support radiologists in verifying their first viewpoint.

Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images

IntroductionIn humanity’s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. ObjectivesEngineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. MethodsThis study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. ResultsIn addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. ConclusionsSuch robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.

JAYA-Optimized Fuzzy Reinforcement Learning Classifier for COVID-19

In the proposed work, Reinforcement Learning has been used to identify Coronavirus disease (COVID-19) amongst viral pneumonia and normal conditions using chest X-ray images. Here, the JAYA-Optimized Fuzzy Reinforcement Learning algorithm, in conjunction with Wavelet Transform-based feature extraction and Principal Component Analysis-based feature reduction technique, has been applied to construct the proposed classifier. Predication accuracy achieved with the proposed approach for COVID-19, viral pneumonia, and normal cases is 87.75%, 95.63%, and 96.89%, respectively. The average classification accuracy is 93.43%. The results show that the JAYA-Optimized Fuzzy Reinforcement Learning approach attains high accuracy and is superior to other contemporary classifiers, i.e. Support Vector Machine, Artificial Neural Network, Tree Algorithm, and K-Nearest Neighbor. The work aims to develop a classifier that can segregate COVID-19 cases from normal and viral pneumonia cases based on the chest X-ray images and is online, adaptive, accurate, and fast. The classifier could serve as a diagnostic tool for healthcare professionals in the current pandemic.

Covid tongue: A new indicator of COVID-19 infection-A case report

Handful cases of anonymous viral pneumonia were registered in China in December 2019. The pathogen, a novel coronavirus called severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), was isolated from infected patient’s lower respiratory tract samples, and was referred to as Coronavirus Disease 2019 (COVID-19).So far, insufficient importance has been given to the prevalence of fungal infections in patients infected with COVID-19 who may experience lymphocytopaenia, hospitalization in intensive care unit (ICU), broad-spectrum antibiotic and corticosteroid usage, intubation, cytokine storms, and possessing chronic illness which make them seriously immunocompromised.Here, we present a case associated with oral lesions manifested by this virus.

Classifying COVID-19 and Viral Pneumonia Lung Infections through Deep Convolutional Neural Network Model using Chest X-Ray Images.

Context:Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose.Aims:This study aims for automated detection and classification of COVID-19 and viral pneumonia diseases by applying deep CNN model using chest X-ray images. The proposed model performs multiclass classification to meet the purpose.Settings and Design:The proposed model is built on top of VGG16 architecture with pretrained ImageNet weights. The model was fine-tuned using additional custom layers to deliver better performance specific to the target.Subjects and Methods:A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model.Statistical Analysis Used:Performance of the model is evaluated through accuracy, precision, recall, and F1 score performance metrics. The results produced by the model are also compared with other recent leading studies.Results:The proposed model has achieved a classification accuracy of 98% with 98% precision, 96% recall, and 97% F1 score on the test dataset for multiclass classification. The area under receiver operating characteristic curve score was 0.99 for all three cases of multiclass classification.Conclusions:The proposed classification model may be highly useful for the preliminary diagnosis of COVID-19 and viral pneumonia cases, especially during heavy workloads and large quantities.