Search

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model's experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. [ABSTRACT FROM AUTHOR]

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death. COVID-19 classification remains challenging due to the ambiguity and similarity with other known respiratory diseases such as SARS, MERS, and other viral pneumonia. The typical symptoms of COVID-19 are fever, cough, chills, shortness of breath, loss of smell and taste, headache, sore throat, chest pains, confusion, and diarrhoea. This research paper suggests the concept of transfer learning using the deterministic algorithm in all binary classification models and evaluates the performance of various CNN architectures. The datasets of 746 CT images of COVID-19 and non-COVID-19 were divided for training, validation, and testing. Various augmentation techniques were applied to increase the number of datasets except for testing images. The images were then pretrained using CNN to obtain a binary class. ResNeXt101 and ResNet152 have the best F1 score of 0.978 and 0.938, whereas GoogleNet has an F1 score of 0.762. ResNeXt101 and ResNet152 have an accuracy of 97.81% and 93.80%. ResNeXt101, DenseNet201, and ResNet152 have 95.71%, 93.81%, and 90% sensitivity, whereas ResNeXt101, ResNet101, and ResNet152 have 100%, 99.58%, and 98.33 specificity, respectively. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16. [ABSTRACT FROM AUTHOR]

Automated diagnosis based on medical images is a very promising trend in modern healthcare services. For the task of automated diagnosis, there should be flexibility to deal with an enormous amount of data represented in the form of medical images. In addition, efficient algorithms that could be adapted according to the nature of images should be used. The importance of automated medical diagnosis has been maximized with the evolution of COVID-19 pandemic. COVID-19 first appeared in China, Wuhan, and then it has exploded in the whole world with a very bad impact on our daily life. The third wave of COVID-19 in the third world is really a disaster in current days, especially with the emergence of the delta variant of COVID-19 that is widespread. Required inspections should be carried out to monitor the COVID-19 spread in daily life and allow primary diagnosis of suspected cases, and long-term clinical laboratory monitoring. Healthcare professionals or radiologists can exploit AI (Artificial Intelligence) tools to quickly and reliably identify the cases of COVID-19. This paper introduces a DCNN (Deep Convolutional Neural Network) framework for chest X-ray and CT image classification based on TL (Transfer Learning). The objective is to perform multi-class and binary classification of the images in order to determine pneumonia and COVID-19 case. The TL is feasible, when using a small dataset by transferring knowledge from natural image classification to medical image classification. Two types of TL are used. The first type is fine-tuning of the DenseNet121, Densenet169, DenseNet201, ResNet50, ResNet152, VGG16, and VGG19 models. The second type is deep tuning of the LeNet-5, AlexNet, Inception naïve v1, and VGG16 models. Extensive tests have been carried out on datasets of chest X-ray and CT images with different training/testing ratios of 80%:20%, 70%:30%, and 60%:40%. Experimental results on 9,270 chest X-ray ray and 2,762 chest CT images acquired from different institutions show that the TL is effective with an average accuracy of 98.49%. [ABSTRACT FROM AUTHOR]

Recently coronavirus 2019 (COVID-2019), discovered in Wuhan city of China in December 2019 announced as world pandemic by the World Health Organization (WHO). It has catastrophic impacts on daily lives, public health, and the global economy. The detection of coronavirus (COVID− 19) is now a critical task for medical specialists. Laboratory methods for detecting the virus such as Polymerase Chain Reaction, antigens, and antibodies have pros and cons represented in time required to obtain results, accuracy, cost and suitability of the test to phase of infection. The need for accurate, fast, and cheap auxiliary diagnostic tools has become a necessity as there are no accurate automated toolkits available. Other medical investigations such as chest X−ray and Computerized Tomography scans are imaging techniques that play an important role in the diagnosis of COVID− 19 virus. Application of advanced artificial intelligence techniques for processing radiological imaging can be helpful for the accurate detection of this virus. However, Due to the small dataset available for COVID− 19, transfer learning from pre−trained convolution neural networks, CNNs can be used as a promising solution for diagnosis of coronavirus. Transfer learning becomes an effective mechanism by transferring knowledge from generic object recognition tasks to domain-specific tasks. Hence, the main contribution of this paper is to exploit the pre−trained deep learning CNN architectures as a cornerstone to enhance and build up an automated tool for detection and diagnosis of COVID− 19 in chest X−Ray and Computerized Tomography images. The main idea is to make use of their convolutional neural network structure and its learned weights on large datasets such as ImageNet. Moreover, a modification to ResNet50 is proposed to classify the patients as COVID infected or not. This modification includes adding three new layers, named, ′Conv′, ′Batch_Normaliz′ and ′Activation_Relu′ layers. These layers are injected in the ResNet50 architecture for accurate discrimination and robust feature extraction. Extensive experiments are carried out to assess the performance of the proposed model on COVID− 19 chest X−Ray and Computerized Tomography scan images. Experimental results approve that the proposed modification, injected layers, increases the diagnosis accuracy to 97.7% for Computerized Tomography dataset and 97.1% for X−Ray dataset which is superior compared to other approaches. [ABSTRACT FROM AUTHOR]

Fangcang shelter (Cabin) hospitals were set up in order to cope with the rapid growth of confirmed cases of coronavirus disease 2019 (COVID‐19) in Wuhan, China at a time when there were insufficient beds in designated hospitals. This paper describes the layout and functioning of a typical Fangcang shelter hospital, Wuhan Dongxihu Fangcang shelter Hospital, where the author has worked, the working mechanism, experience and effectiveness. A set of patient management protocols was employed for daily practice, which included preset criteria and procedure for admission, examination, medication treatment, referral and discharge. WeChat platform with different groups was used for communication, ward round, test appointments and patient data communication. All these procedures and mechanisms of working enabled the effective management of a larger number of patients with relatively few doctors. As a result, 442 mild or moderate COVID‐19 patients in Hall C were successfully managed by a team of 40 doctors, with 246 (56%) patients were cured and discharged from the Fangcang shelter hospital while the remaining 196 (44%) patients were referred on to designated hospitals for further treatment. The reasons for referral included poor resolution in computerized tomography (CT) scan (59%), persistently positive severe acute respiratory syndrome coronavirus 2 by PCR after 9 days of admission (16%), deterioration in CT image (4%), development of dyspnoea (1%) and other (4%) or unclear reasons (16%) due to no record of reasons for referral on the document. There were no deaths and no complaints from the patients in Hall C. In summary, the Fangcang shelter hospital could be run successfully with a set of patient management protocols under conditions of limited facilities and medical staff. It was effective and safe in isolating patients, providing basic medical care and early identification of potential severe cases. This experience may provide a successful example of a working mechanism for the prevention and control of the COVID‐19 pandemic worldwide. [ABSTRACT FROM AUTHOR]

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets. [ABSTRACT FROM AUTHOR]

The accurate knowledge of demographic, signs and symptoms, imaging characteristics of coronavirus disease 2019 (COVID-19) is essential for the accurate management of these patients. However, the claims between the previous papers are not always consistent and may even contradict each other, for example, some claims the virus infects more men than women in Wuhan. In this large-scale cohort study, we aimed to update the demographic, signs and symptoms, imaging characteristics of patients with COVID-19 in the whole quarantine of Wuhan, China. A cohort of 2126 patients with a diagnosis of COVID-19 pneumonia (confirmed by real-time reverse transcriptase-polymerase chain reaction, RT-PCR) who were admitted to one hospital in Wuhan were retrospectively enrolled. Data were collected between January 13, 2020, and April 8, 2020, the end of Wuhan quarantine. Demographic, signs and symptoms, imaging characteristics were analyzed. CT imaging characteristics associated with respiratory failure or death were identified. Of the 2126 patients with COVID-19, 1051 (49.44%) were men and 1075 (50.56%) were women, 1933 (90.92%) have fever and 1328 (62.46%) have dry cough. The mean age was 57.43 years of age (range 1–95). The CT imaging findings were bilateral pneumonia (1883[88.57%]), unilateral pneumonia (243[11.43%]), ground-glass opacity (GGO) or consolidation (1175[55.27%]), pleural effusion (69[3.25%]). Patients with respiratory failure or death were more likely to have pleural effusion on CT than patients without respiratory failure or death (p < 0.05). Men and women have been infected by SARS-CoV-2 in roughly equal numbers. Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. • Men and women have been infected by SARS-CoV-2 in roughly equal numbers. • Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. • COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. [ABSTRACT FROM AUTHOR]

• Chest CT scan images obtained from patients infected with the Coronavirus (COVID-19) were used. • In this study, we detected with ELM and Deep Neural Network using COVID-19, normal, and pneumonia chest CT scan data. • A novel Multiple Kernels-ELM-based Deep Neural Network model is presented. • The proposed model achieved 98.36% classification accuracy for COVID-19 detection by using lung CT scan images. Coronavirus disease is a fatal epidemic that has originated in Wuhan, China in December 2019. This disease is diagnosed using radiological images taken with the help of basic scanning methods besides the test kits for Reverse Transcription Polymerase Chain Reaction (RT-PCR). Automatic analysis of chest Computed Tomography (CT) images that are based on image processing technology plays an important role in combating this infectious disease. In this paper, a new Multiple Kernels-ELM-based Deep Neural Network (MKs-ELM-DNN) method is proposed for the detection of novel coronavirus disease - also known as COVID-19, through chest CT scanning images. In the model proposed, deep features are extracted from CT scan images using a Convolutional Neural Network (CNN). For this purpose, pre-trained CNN-based DenseNet201 architecture, which is based on the transfer learning approach is used. Extreme Learning Machine (ELM) classifier based on different activation methods is used to calculate the architecture's performance. Lastly, the final class label is determined using the majority voting method for prediction of the results obtained from each architecture based on ReLU-ELM, PReLU-ELM, and TanhReLU-ELM. In experimental works, a public dataset containing COVID-19 and Non-COVID-19 classes was used to verify the validity of the MKs-ELM-DNN model proposed. According to the results obtained, the accuracy score was obtained as 98.36% using the MKs-ELM-DNN model. The results have demonstrated that, when compared, the MKs-ELM-DNN model proposed is proven to be more successful than the state-of-the-art algorithms and previous studies. This study shows that the proposed Multiple Kernels-ELM-based Deep Neural Network model can effectively contribute to the identification of COVID-19 disease. [ABSTRACT FROM AUTHOR]

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts. [ABSTRACT FROM AUTHOR]

The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited. [ABSTRACT FROM AUTHOR]

COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5-6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is a novel approach combining residual attention with deep support vector data description (DSVDD) to classify the CT images. To the best of our knowledge, we are the first to combine residual attention with DSVDD in general, and specifically in the diagnosis of COVID-19. Combining attention with DSVDD naively may cause model collapse. Attention in the proposed RADSVDD guides the network during training and enables quick learning, residual connectivity prevents vanishing gradients. Our approach consists of three models, each model is devoted to recognizing one certain disease and classifying other diseases as anomalies. These models learn in an end-to-end fashion. The proposed approach attained high performance in classifying CT images into intact, COVID-19, and non-COVID-19 pneumonia. To evaluate the proposed approach, we created a dataset from published datasets and had it assessed by an experienced radiologist. The proposed approach achieved high performance, with the normal model attained sensitivity (0.96-0.98), specificity (0.97-0.99), F1-score (0.97-0.98), and area under the receiver operator curve (AUC) 0.99; the COVID-19 model attained sensitivity (0.97-0.98), specificity (0.97-0.99), F1-score (0.97-0.99), and AUC 0.99; and the non-COVID pneumoniamodel attained sensitivity (0.97-1), specificity (0.98-0.99), F1-score (0.97-0.99), and AUC 0.99. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic, which first appeared in Wuhan, China, in December 2019 and spread rapidly around the globe, continues to be a serious threat today. Rapid and accurate diagnostic methods are needed to identify, isolate and treat patients as soon as possible because of the rapid contagion of COVID‐19. In the present study, the relation of the semi‐quantitative scoring method with computed tomography in the diagnosis of COVID‐19 in determining the severity of the disease with clinical and laboratory parameters and survival of the patients were investigated along with its value in prognostic prediction. Material and method: A total of 277 adult patients who were followed up in the chest diseases clinic because of COVID‐19 pneumonia between 11.03.2020 and 31.05.2020 were evaluated retrospectively in the present study. Both lungs were divided into five regions in line with their anatomical structures, and semiquantitative radiological scoring was made between 0 and 25 points according to the distribution of lesions in each region. The relations between semiquantitative radiological score and age, gender, comorbidity, and clinical and laboratory parameters were examined. Results: A significant correlation was detected between advanced age, lymphopenia, low oxygen saturation, high ferritin, D‐dimer, and radiological score in the univariate analysis performed in the present study. The cut‐off value of the semiquantitative radiology score was found to be 15 (AUC: 0.615, 95% CI: 0.554–0.617, p = 0.106) in ROC analysis. The survival was found to be better in cases with a radiology score below 15, in Kaplan–Meier analysis (HR: 4.71, 95% CI: 1.43–15.46, p < 0.01). In the radiological score and nonparametric correlation analyses, positive correlations were detected between CRP, D‐dimer, AST, LDH, ferritin, and pro‐BNP, and a negative correlation was found between partial oxygen pressure and oxygen saturation (p = 0.01, r = 0.321/0.313/0.362/0.343/0.313/0.333/−0.235/−0.231, respectively) Conclusion: It was found that the scoring system that was calculated quantitatively in thorax HRCTs in Covid‐19 patients is a predictive actor in determining the severity and prognosis of the disease in correlation with clinical and laboratory parameters. Considering patients who have a score of 15 and above with semiquantitative scoring risky in terms of poor prognosis and short survival and close follow‐up and early treatment may be effective to reduce mortality rates. [ABSTRACT FROM AUTHOR]

• This review mainly reviewed the diagnostic techniques of COVID-19. • Sampling and sample treatment greatly affect nucleic acid testing (NAT) results. • Novel NAT assays such as CRISPR/Cas-based assays have been developed. • NAT, immunological methods, and CT imaging have different roles in COVID-19 diagnostics. • A combination of NAT, immunological methods, and CT imaging improves diagnostic accuracy. COVID-19, a new respiratory infectious disease, was first reported at the end of 2019, in Wuhan, China. Now, COVID-19 is still causing major loss of human life and economic productivity in almost all countries around the world. Early detection, early isolation, and early diagnosis of COVID-19 patients and asymptomatic carriers are essential to blocking the spread of the pandemic. This paper briefly reviewed COVID-19 diagnostic assays for clinical application, including nucleic acid tests, immunological methods, and Computed Tomography (CT) imaging. Nucleic acid tests (NAT) target the virus genome and indicates the existence of the SARS-CoV-2 virus. Currently, real-time quantitative PCR (qPCR) is the most widely used NAT and, basically, is the most used diagnostic assay for COVID-19. Besides qPCR, many novel rapid and sensitive NAT assays were also developed. Serological testing (detection of serum antibodies specific to SARS-CoV-2), which belongs to the immunological methods, is also used in the diagnosis of COVID-19. The positive results of serological testing indicate the presence of antibodies specific to SARS-CoV-2 resulting from being infected with the virus. Viral antigen detection assays are also important immunological methods used mainly for rapid virus detection. However, only a few of these assays had been reported. CT imaging is still an important auxiliary diagnosis tool for COVID-19 patients, especially for symptomatic patients in the early stage, whose viral load is low and different to be identified by NAT. These diagnostic techniques are all good in some way and applying a combination of them will greatly improve the accuracy of COVID-19 diagnostics. [ABSTRACT FROM AUTHOR]

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link. [ABSTRACT FROM AUTHOR]

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link. [ABSTRACT FROM AUTHOR]

Background: In December 2019, a cluster of unknown etiology pneumonia cases occurred in Wuhan, China leading to identification of the responsible pathogen as SARS-coV-2. Since then, the coronavirus disease 2019 (COVID-19) has spread to the entire world. Computed Tomography (CT) is frequently used to assess severity and complications of COVID-19 pneumonia. The purpose of this study is to compare the CT patterns and clinical characteristics in intensive care unit (ICU) and non- ICU patients with COVID-19 pneumonia. Design and Methods: This retrospective study included 218 consecutive patients (136 males; 82 females; mean age 63±15 years) with laboratory-confirmed SARS-coV-2. Patients were categorized in two different groups: (a) ICU patients and (b) non-ICU inpatients. We assessed the type and extent of pulmonary opacities on chest CT exams and recorded the information on comorbidities and laboratory values for all patients. Results: Of the 218 patients, 23 (20 males: 3 females; mean age 60 years) required ICU admission, 195 (118 males: 77 females, mean age 64 years) were admitted to a clinical ward. Compared with non-ICU patients, ICU patients were predominantly males (60% versus 83% p=0.03), had more comorbidities, a positive CRP (p=0.04) and higher LDH values (p=0.008). ICU patients' chest CT demonstrated higher incidence of consolidation (p=0.03), mixed lesions (p=0.01), bilateral opacities (p<0.01) and overall greater lung involvement by consolidation (p=0.02) and GGO (p=0.001). Conclusions: CT imaging features of ICU patients affected by COVID-19 are significantly different compared with non-ICU patients. Identification of CT features could assist in a stratification of the disease severity and supportive treatment. [ABSTRACT FROM AUTHOR]

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT). CT images provide valuable information on how this new disease affects the lungs of patients. However, the analysis of these images is not trivial, especially when researchers are searching for quick solutions. Detecting and evaluating this disease can be tiring, time-consuming, and susceptible to errors. Thus, in this study, we aim to automatically segment infections caused by COVID19 and provide quantitative measures of these infections to specialists, thus serving as a support tool. We use a database of real clinical cases from Pedro Ernesto University Hospital of the State of Rio de Janeiro, Brazil. The method involves five steps: lung segmentation, segmentation and extraction of pulmonary vessels, infection segmentation, infection classification, and infection quantification. For the lung segmentation and infection segmentation tasks, we propose modifications to the traditional U-Net, including batch normalization, leaky ReLU, dropout, and residual block techniques, and name it as Residual U-Net. The proposed method yields an average Dice value of 77.1% and an average specificity of 99.76%. For quantification of infectious findings, the proposed method achieves results like that of specialists, and no measure presented a value of ρ < 0.05 in the paired t-test. The results demonstrate the potential of the proposed method as a tool to help medical professionals combat COVID-19. fight the COVID-19. [ABSTRACT FROM AUTHOR]

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: The recent outbreak of pneumonia cases in Wuhan, PR China, was caused by a novel beta coronavirus, the 2019 novel coronavirus (COVID-19). Purpose: To summarize chest computed tomography (CT) manifestations of the early stage of COVID-19 infection and provide a piece of reliable imaging evidence for initial screening and diagnosis. Material and Methods: From 10 January 2020 to 10 February 2020, we continuously observed chest CT imaging of 14 patients with clinically suspected new coronavirus infection in the two weeks after onset of symptoms. Ground-glass opacity (GGO), consolidation, reticular pattern, and ground-glass mimic nodules in each patient's chest CT image were recorded. Results: We enrolled 14 patients, of which nine patients had the infection confirmed by reverse transcription polymerase chain reaction (RT-PCR). Five patients were highly suspected of infection. All cases had epidemiological evidence. GGO was a dominant imaging manifestation in the initial days of infection. GGO performance accounts for 40% in 1-2 days, 90% in 3-6 days, and 85% in 7-10 days. With disease progression, consolidation appeared on follow-up CT. Consolidation performance accounts for 0% in 1-2 days, 40% in 3-6 days, and 71% in 7- 10 days. The lesions are mostly near the pleura. The number of lesions and the extent of the lesions increased as the disease progressed. Conclusion: Patients with novel coronavirus pneumonia have characteristic CT features in the initial stage of infection, which can be used as an essential supplement for nucleic acid examination. [ABSTRACT FROM AUTHOR]

COVID-19 (coronavirus disease 2019) is a recently emerged pulmonary infection caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). It started in Wuhan, China, in December 2019 and led to a highly contagious disease. Since then COVID-19 continues to spread, causing exponential morbidity and mortality and threatening economies worldwide. While the primary diagnostic test for COVID-19 is the reverse transcriptase–polymerase chain reaction (RT-PCR) assay, chest CT has proven to be a diagnostic tool of high sensitivity. A variety of conditions demonstrates CT features that are difficult to differentiate from COVID-19 rendering CT to be of low specificity. Radiologists and physicians should be aware of imaging patterns of these conditions to prevent an erroneous diagnosis that could adversely influence management and patients' outcome. Our purpose is to provide a practical review of the conditions that mimic COVID-19. A brief description of the forementioned clinical conditions with their CT features will be included. [ABSTRACT FROM AUTHOR]

Objectives: To analyse clinical and radiological changes from disease onset to exacerbation in coronavirus infectious disease-19 (COVID-19) patients.Methods: We reviewed clinical histories of 276 patients with confirmed COVID-19 pneumonia and extracted data on patients who met the diagnostic criteria for COVID-19 severe/fatal pneumonia and had an acute exacerbation starting with mild or common pneumonia.Results: Twenty-four patients were included. Of these, 8% were smokers, 54% had been to Wuhan, and 46% had comorbidities. Before acute exacerbation, elevated lactate dehydrogenase (232.9 ± 88.7) was present, and chest CT scans showed the number of involved lobes was 4 (2-5) and total CT score was 6 (2-8). Following acute exacerbation, patients were likely to have more clinical symptoms (p < 0.01) and abnormal laboratory changes (p < 0.01). The number of involved lobes and CT score after an exacerbation significantly increased to 5 (5-5) and 12 (9-14), respectively. Receiver operating characteristic (ROC) curve showed that, when the cutoff value of CT score was 5, the sensitivity and specificity for severe pneumonia were 90% and 70%, respectively. CT findings of ground glass opacity with consolidations (91.7%), bilateral distribution (100.0%), and multifocal lesion (100.0%) were features in found in patients after exacerbation.Conclusions: There are significant changes in clinical, laboratory, and CT findings in patients from disease onset to exacerbation. An increase in the number of involved lobes or an increased CT score from the baseline may predict poor clinical outcomes. Combining an assessment of CT changes with clinical and laboratory changes could help clinical teams evaluate the prognosis.Key Points: • The common chest CT signs of COVID-19 pneumonia after exacerbation were ground glass opacity (GGO) with consolidation, bilateral distribution, and multifocal lesions. • An increase in number of involved lobes or an increased CT score from the baseline may predict a poor clinical outcome. • Worsened symptoms and abnormal laboratory results are also associated with poor prognosis. [ABSTRACT FROM AUTHOR]

Objectives: To investigate CT images of 100 confirmed COVID-19 pneumonia patients to describe the lesion distribution, CT signs, and evolution during different courses. Methods: A retrospective study of 100 COVID-19 pneumonia patients without ARDS was performed, and CT scans were reviewed. A COVID-19 pneumonia course diagram was drawn. Mann-Whitney U test was used to compare the lesion distribution and CT scores, χ2 test was used to compare the CT findings between different stages. Results: A total of 272 CT scans from 100 patients (mean age, 52.3 years ± 13.1) were investigated. Four patients with lung abnormalities on CT first showed negative RT-PCR result and turned positive afterwards. One hundred sixty-nine (62.1%) showed predominantly peripheral distribution. The CT scores of the upper zone (3.4 ± 3.6) were significantly lower than those of the middle (5.0 ± 3.9) and lower (4.8 ± 3.6) zones (p < 0.001). The CT scores of the anterior zones (4.9 ± 4.7) were significantly lower than those of the posterior zones (8.4 ± 6.2) (p < 0.001). In the early rapid progressive stage (1~7 days), ground glass opacity (GGO) plus reticular pattern (58.1%), GGO plus consolidation (43.0%), and GGO (41.9%) were all common. In the advanced stage (8~14 days), GGO plus consolidation (79.8%) and repairing CT signs (subpleural line, bronchus distortion, and fibrotic strips) showed a significant increase (p < 0.05). In the absorption stage, GGO plus consolidation (9.1%) sharply decreased (p < 0.05). Conclusion: CT imaging of COVID-19 pneumonia showed a predominantly peripheral, middle and lower, and posterior distribution. The early rapid progressive stage is 1~7 days from symptom onset, the advanced stage with peak levels of abnormalities on CT is 8~14 days, and the abnormalities started to improve after 14 days. Key Points: • The course of COVID-19 pneumonia consists of three stages: 1~7 days is the early rapid progressive stage, 8~14 days is the advanced stage, and after 14 days, the abnormalities started to decrease. • In the early rapid progressive stage, GGO plus a reticular pattern, GGO plus consolidation, and GGO were all common signs; in the advanced stage, signs of progression and absorption coexisted; lung abnormalities showed an asynchronous process with parts with absorption and parts progressing. • Lung abnormalities mainly showed predominantly peripheral, middle, and lower distribution. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to observe the imaging characteristics of the novel coronavirus pneumonia.Methods: Sixty-three confirmed patients were enrolled from December 30, 2019 to January 31, 2020. High-resolution CT (HRCT) of the chest was performed. The number of affected lobes, ground glass nodules (GGO), patchy/punctate ground glass opacities, patchy consolidation, fibrous stripes and irregular solid nodules in each patient's chest CT image were recorded. Additionally, we performed imaging follow-up of these patients.Results: CT images of 63 confirmed patients were collected. M/F ratio: 33/30. The mean age was 44.9 ± 15.2 years. The mean number of affected lobes was 3.3 ± 1.8. Nineteen (30.2%) patients had one affected lobe, five (7.9%) patients had two affected lobes, four (6.3%) patients had three affected lobes, seven (11.1%) patients had four affected lobes while 28 (44.4%) patients had 5 affected lobes. Fifty-four (85.7%) patients had patchy/punctate ground glass opacities, 14 (22.2%) patients had GGO, 12 (19.0%) patients had patchy consolidation, 11 (17.5%) patients had fibrous stripes and 8 (12.7%) patients had irregular solid nodules. Fifty-four (85.7%) patients progressed, including single GGO increased, enlarged and consolidated; fibrous stripe enlarged, while solid nodules increased and enlarged.Conclusions: Imaging changes in novel viral pneumonia are rapid. The manifestations of the novel coronavirus pneumonia are diverse. Imaging changes of typical viral pneumonia and some specific imaging features were observed. Therefore, we need to strengthen the recognition of image changes to help clinicians to diagnose quickly and accurately.Key Points: • High-resolution CT (HRCT) of the chest is critical for early detection, evaluation of disease severity and follow-up of patients with the novel coronavirus pneumonia. • The manifestations of the novel coronavirus pneumonia are diverse and change rapidly. • Radiologists should be aware of the various features of the disease and temporal changes. [ABSTRACT FROM AUTHOR]

Background: Compared to adults, there are relatively few studies on COVID-19 infection in children, and even less focusing on the unique features of COVID-19 in children in terms of laboratory findings, locations of computerized tomography (CT) lesions, and the role of CT in evaluating clinical recovery. The objective of this study is to report the results from patients at Wuhan Children's Hospital, located within the initial center of the outbreak.Methods: Clinical, imaging, and laboratory data of 76 children were collected retrospectively and analyzed with the Fisher exact test and Cox regression statistical methods.Results: Among 50 children with a positive COVID-19 real-time reverse-transcriptase polymerase chain reaction (PCR), five had negative PCR results initially but showed positive results in subsequent tests. Eight (16%) patients had lymphopenia, seven (14%) with thrombocytopenia, four (8%) with lymphocytosis, two (4%) with thrombocytosis, ten (20%) with elevated C-reactive protein, four (8%) with hemoglobin above, and six (12%) with below standard reference values. Seven (14%) of the 50 had no radiologic evidence of disease on chest CT. For the 43 patients who had abnormal CT findings, in addition to previously reported patterns of ground-glass opacity (67%), local patchy shadowing (37%), local bilateral patchy shadowing (21%), and lesion location of lower lobes (65%), other CT features include that an overwhelming number of pediatric patients had lesions in the subpleural area (95%) and 22 of the 28 lower lobe lesions were in the posterior segment (78%). Lesions in most of the 15 patients (67%) who received chest CT at discharge were not completely absorbed, and 26% of these pediatric patients had CT lesions that were either unchanged or worse.Conclusions: There were a few differences between COVID-19 children and COVID-19 adults in terms of laboratory findings and CT characteristics. CT is a powerful tool to detect and characterize COVID-19 pneumonia but has little utility in evaluating clinical recovery for children. These results oppose current COVID-19 hospital discharge criteria in China, as one requirement is that pulmonary imaging must show significant lesion absorption prior to discharge. These differences between pediatric and adult cases of COVID-19 may necessitate pediatric-specific discharge criteria. [ABSTRACT FROM AUTHOR]

The aim of this study was to describe clinical, imaging, and laboratory features of acute pulmonary embolism (APE) in patients with COVID-19 associated pneumonia. Patients with COVID-19 associated pneumonia who underwent a computed tomography pulmonary artery (CTPA) scan for suspected APE were retrospectively studied. Laboratory data and CTPA images were collected. Imaging characteristics were analyzed descriptively. Laboratory data were analyzed and compared between patients with and without APE. A series of 25 COVID-19 patients who underwent CTPA between January 2020 and February 2020 were enrolled. The median D-dimer level founded in these 25 patients was 6.06 μg/mL (interquartile range [IQR] 1.90-14.31 μg/mL). Ten (40%) patients with APE had a significantly higher level of D-dimer (median, 11.07 μg/mL; IQR, 7.12-21.66 vs median, 2.44 μg/mL; IQR, 1.68-8.34, respectively, P =.003), compared with the 15 (60%) patients without APE. No significant differences in other laboratory data were found between patients with and without APE. Among the 10 patients with APE, 6 (60%) had a bilateral pulmonary embolism, while 4 had a unilateral embolism. The thrombus-prone sites were the right lower lobe (70%), the left upper lobe (60%), both upper lobe (40%) and the right middle lobe (20%). The thrombus was partially or completely absorbed after anticoagulant therapy in 3 patients who underwent a follow-up CTPA. Patients with COVID-19 associated pneumonia have a risk of developing APE during the disease. When the D-dimer level abnormally increases in patients with COVID-19 pneumonia, CTPA should be performed to detect and assess the severity of APE. [ABSTRACT FROM AUTHOR]