Viral Pneumonia Diseases Research Articles

An automated system to distinguish between Corona and Viral Pneumonia chest diseases based on image processing techniques

ABSTRACT Recently, huge concerns have been raised in diagnosing chest diseases, especially after the COVID-19 pandemic. Regular diagnosis processes of chest diseases sometimes fail to distinguish between Corona and Viral Pneumonia diseases through Polymerase Chain Reaction (PCR) tests which are a time-engrossing process that needs convoluted manual procedures. Artificial Intelligence (AI) techniques have achieved high performance in aiding medical diagnostic processes. The innovation of this work lies in using a new diagnostic technique to distinguish between COVID-19 and Viral Pneumonia diseases using advanced AI technologies. This is done by extracting novel features from chest X-ray images based on Wavelet analysis, Scale Invariant Feature Transformation (SIFT), and the Mel Frequency Cepstral Coefficient (MFCC). Support vector machines (SVM) and artificial neural networks (ANN) were utilized to build classification algorithms using 1200 chest X-ray mages for each case. Using Wavelet features, the results of evaluating the SVM and ANN models were 97% accurate, and with SIFT features, they were closer to 99%. The proposed models were very effective at identifying COVID-19 and Viral Pneumonitis, so physicians can determine the best treatment course for patients with the support of this high accuracy. Moreover, this model can be used in hospitals and emergency rooms when a massive number of patients are waiting, as it is faster and more accurate than the regular diagnosis processes as each step takes few seconds on average to complete.

Computer-assisted lung diseases detection from pediatric chest radiography using long short-term memory networks

Pneumonia is one of the most common lung diseases for children. Computer-aided diagnosis (CAD) can be utilized to achieve automated lung disease detection using artificial intelligence techniques. This paper proposes a hybrid deep model of pretrained convolutional neural network (CNN) and long short-term memory (LSTM) networks to identify bacterial pneumonia versus viral pneumonia diseases in pediatric chest radiography. Extensive experiments have been conducted in two main scenarios, namely, pneumonia versus normal cases and bacterial pneumonia versus viral pneumonia infections. Evaluation of the results showed that the proposed deep classifiers achieved an accuracy and area under curve (AUC) of 98.6% and 99.9%, respectively, for normal versus pneumonia scenario. Meanwhile, the obtained accuracy and AUC are 92.3% and 94.5%, respectively, for bacterial pneumonia versus viral pneumonia classification. Compared to deep models in previous studies, our hybrid classifiers of CNN and LSTM networks have relatively better performance to assist radiologists during the scanning procedure of the child's lungs.

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.

COVID-19 and Pneumonia Diagnosis in X-Ray Images Using Convolutional Neural Networks

This paper proposes a Convolutional Neural Networks (CNN) based model for the diagnosis of COVID-19 and non-COVID-19 viral pneumonia diseases. These diseases affect and damage the human lungs. Early diagnosis of patients infected by the virus can help save the patient’s life and prevent the further spread of the virus. The CNN model is used to help in the early diagnosis of the virus using chest X-ray images, as it is one of the fastest and most cost-effective ways of diagnosing the disease. We proposed two convolutional neural networks (CNN) models, which were trained using two different datasets. The first model was trained for binary classification with one of the datasets that only included pneumonia cases and normal chest X-ray images. The second model made use of the knowledge learned by the first model using transfer learning and trained for 3 class classifications on COVID-19, pneumonia, and normal cases based on the second dataset that included chest X-ray (CXR) images. The effect of transfer learning on model constriction has been demonstrated. The model gave promising results in terms of accuracy, recall, precision, and F1_score with values of 98.3%, 97.9%, 98.3%, and 98.0%, respectively, on the test data. The proposed model can diagnose the presence of COVID-19 in CXR images; hence, it will help radiologists make diagnoses easily and more accurately.

Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.

Recent findings of coronavirus: the pathogenesis and treatment

Coronavirus disease-2019 (COVID-19) is a type of acute viral pneumonia, which began with a sudden outbreak in Wuhan, China Although closely related to severe acute respiratory syndrome, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), the disease excessive transmission rate distinguishes it from other viral pneumonia diseases The pulmonary complications and infections caused by this virus, as well as the lack of specific therapeutic have caused one of the most complicated epidemics of the last century In this review article, we attempted to discuss and describe the latest findings of immunopathogenesis, as well new and applicable treatments for coronavirus disease

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

Clinical Characteristics of Hospitalized Patients with Viral Pneumonia Diseases, September 2017 – April 2018

Clinical Characteristics of Hospitalized Patients with Viral Pneumonia Diseases, September 2017 – April 2018