Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.

Deep learning techniques for detecting and recognizing face masks: A survey

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19;we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification.

To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19.

Covid-19 has become a pandemic that affects lots of individuals daily, worldwide, and, particularly, the widespread disruption in numerous countries, namely, the US, Italy, India, Saudi Arabia. The timely detection of this infectious disease is mandatory to prevent the quick spread globally and locally. Moreover, the timely detection of COVID-19 in the coming time is significant to well cope with the disease control by Governments. The common symptoms of COVID are fever as well as dry cough, which is similar to the normal flu. The disease is devastating and spreads quickly, which affects individuals of all ages, particularly, aged people and those with feeble immune systems. There is a standard method employed to detect the COVID, namely, the real-time polymerase chain reaction (RT-PCR) test. But this method has shortcomings, i.e., it takes a long time and generates maximum false-positive cases. Consequently, we necessitate to propose a robust framework for the detection as well as for the estimation of COVID cases globally. To achieve the above goals, we proposed a novel technique to analyze, predict, and detect the COVID-19 infection. We made dependable estimates on significant pandemic parameters and made predictions of infection as well as potential washout time frames for numerous countries globally. We used a publicly available dataset composed by Johns Hopkins Center for estimation, analysis, and predictions of COVID cases during the time period of 21 April 2020 to 27 June 2020. We employed a simple circulation for fast as well as simple estimates of the COVID model and estimated the parameters of the Gaussian curve, utilizing a parameter, namely, the least-square parameter curve fitting for numerous countries in distinct areas. Forecasts of COVID depend upon the potential results of Gaussian time evolution with a central limit theorem of data the Covid prediction to be justified. For gaussian distribution, the parameters, namely, extreme time and thickness are regulated using a statistical Y2 fit for the aim of doubling times after 21 April 2020. Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. We fed the extracted features into the proposed COVIDDetectorNet to detect COVID-19, viral pneumonia, and other lung infections. Our method obtained an accuracy of 96.51, 92.62, and 86.53% for two, three, and four classes, respectively. Experimental outcomes illustrate that our method is reliable to be employed for the forecast and detection of COVID-19 disease.

AI-driven deep convolutional neural networks for chest X-ray pathology identification.

BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images. OBJECTIVE: To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays. METHOD: Several CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images. RESULTS: In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes. CONCLUSION: This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images.

Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.

BACKGROUND: COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art techniques and were able to deliver promising results, these techniques are not very useful if they can detect only one type of disease without detecting the others. OBJECTIVE: The main objective of this study was to achieve a fast and more accurate diagnosis of COVID-19. This study proposes a diagnostic technique that classifies COVID-19 x-ray images from normal x-ray images and those specific to 14 other chest diseases. METHODS: In this paper, we propose a novel, multilevel pipeline, based on deep learning models, to detect COVID-19 along with other chest diseases based on x-ray images. This pipeline reduces the burden of a single network to classify a large number of classes. The deep learning models used in this study were pretrained on the ImageNet dataset, and transfer learning was used for fast training. The lungs and heart were segmented from the whole x-ray images and passed onto the first classifier that checks whether the x-ray is normal, COVID-19 affected, or characteristic of another chest disease. If it is neither a COVID-19 x-ray image nor a normal one, then the second classifier comes into action and classifies the image as one of the other 14 diseases. RESULTS: We show how our model uses state-of-the-art deep neural networks to achieve classification accuracy for COVID-19 along with 14 other chest diseases and normal cases based on x-ray images, which is competitive with currently used state-of-the-art models. Due to the lack of data in some classes such as COVID-19, we applied 10-fold cross-validation through the ResNet50 model. Our classification technique thus achieved an average training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (ie, 3 classes). For the second level of classification (ie, 14 classes), our technique achieved a maximum training accuracy of 88.52% and test accuracy of 66.634% by using ResNet50. We also found that when all the 16 classes were classified at once, the overall accuracy for COVID-19 detection decreased, which in the case of ResNet50 was 88.92% for training data and 71.905% for test data. CONCLUSIONS: Our proposed pipeline can detect COVID-19 with a higher accuracy along with detecting 14 other chest diseases based on x-ray images. This is achieved by dividing the classification task into multiple steps rather than classifying them collectively.

Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet.

The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).

Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.

OBJECTIVE: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. METHODS: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. RESULTS: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. CONCLUSION: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.

OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. METHOD: This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study. RESULTS: Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. CONCLUSION: This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia.

Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results. Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. METHODS: Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. RESULTS: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. CONCLUSION: The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation.