Intelligent Facemask Coverage Detector in a World of Chaos

The recent outbreak of COVID-19 around the world has caused a global health catastrophe along with economic consequences. As per the World Health Organization (WHO), this devastating crisis can be minimized and controlled if humans wear facemasks in public;however, the prevention of spreading COVID-19 can only be possible only if they are worn properly, covering both the nose and mouth. Nonetheless, in public places or in chaos, a manual check of persons wearing the masks properly or not is a hectic job and can cause panic. For such conditions, an automatic mask-wearing system is desired. Therefore, this study analyzed several deep learning pre-trained networks and classical machine learning algorithms that can automatically detect whether the person wears the facemask or not. For this, 40,000 images are utilized to train and test 9 different models, namely, InceptionV3, EfficientNetB0, EfficientNetB2, DenseNet201, ResNet152, VGG19, convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), to recognize facemasks in images. Besides just detecting the mask, the trained models also detect whether the person is wearing the mask properly (covering nose and mouth), partially (mouth only), or wearing it inappropriately (not covering nose and mouth). Experimental work reveals that InceptionV3 and EfficientNetB2 outperformed all other methods by attaining an overall accuracy of around 98.40% and a precision, recall, and F1-score of 98.30%.

Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies.

The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance-minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier.

Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging

The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, this paper proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of COVID-19 disease. The study utilized chest X-ray images to segregate COVID-19 positive cases among healthy individuals by exploiting several combinational structures of image filtering, feature-extraction techniques, and machine learning algorithms. It analyzed the effects of three noise removal filters and two feature-extraction techniques on performance of several machine learning and deep-learning-based classifiers. The proposed schemes first remove unnecessary noise using a conservative smoothing filter, Crimmins speckle removal, and Gaussian filter. It then employs linear discriminant analysis (LDA) as linear method and principal component analysis (PCA) as non-linear feature-extraction technique to extract highly discriminant feature sets. Finally, it uses these feature sets to train various classification models, including convolutional neural network (CNN), support vector machine (SVM), and logistic regression (LG). Evidently, the proposed conservative smoothing filter with single peak to maintain symmetry in horizontal and vertical directions for enhancement of image, along with LDA and SVM, secured an overall classification accuracy of 99.93%. Experimental results show that, besides achieving high accuracies, the incorporation of feature-extraction techniques significantly reduces the computational time of the proposed model.

Presenting the characteristics, smoking versus diabetes, and outcome among patients hospitalized with COVID-19.

METHODS: We designed a cross-sectional, observational follow-up for 284 COVID-19 patients involving healthy patients, smokers, diabetics, and diabetic plus smokers recruited from May 1, 2020 to June 25, 2020. The clinical features, severity, duration, and outcome of the disease were analyzed. RESULTS: Of 284 COVID-19 patients, the median age was 48 years (range, 18-80), and 33.80% were female. Common symptoms included fever (85.56%), shortness of breath (49.65%), cough (45.42%), and headache (40.86%). Patients with more than one comorbidity (diabetes and smoking) presented as severe-critical cases compared to healthy patients, diabetics, and smokers. Smokers presented with a lower rate of death in comparison to diabetic patients and diabetic + smoking, furthermore, smoking was less risky than diabetes. Although the mortality rate was high in patients with smokers compared to healthy patients (4.22%, the hazard ratio [HR], 1.358; 95% confidence interval [CI], 1.542-1.100; p = .014), it was less than in diabetics (7.04%, HR 1.531, 95% CI: 1.668-1.337, p = .000), and diabetic plus smoker (10.00%, HR, 1.659; 95% CI, 1.763-1.510; p = .000). CONCLUSION: Multiple comorbidities are closely related to the severity of COVID-19 disease progression and the higher mortality rate. Smokers presented as mild cases compared to diabetic and diabetic + smoking patients, who presented as severe to critical cases. Although a higher death rate in smokers was seen compared with healthy patients, this was smaller when compared to diabetic and diabetic + smoking patients.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.

The prevalence of gastrointestinal symptoms among patients with COVID-19 and the effect on the severity of the disease.

Background and Aim: COVID-19 is a new pandemic disease recognized by the World Health Organization. It mainly affects the respiratory system, but it can also affect other systems. The gastrointestinal system has been found to be affected in many patients. This study investigated the COVID-19-related gastrointestinal manifestations and the effect of gastrointestinal involvement on the course and outcome of the disease. Methods: This was a retrospective descriptive study conducted on 140 COVID-19 polymerase chain reaction-positive symptomatic individuals admitted to Al-Shafa Hospital - Medical City Complex in Baghdad, Iraq during the period 2 March 2020 to 12 May 2020. Demographic data and clinical presentation and laboratory data were extracted from the case sheets of the patients and were also obtained from direct communication with the patients, their families, and medical staff. Results: Gastrointestinal (GI) symptoms alone were detected in 23.6% of the patients; 44.3% of the patients presented with only respiratory symptoms, and 32.1% presented with both respiratory and GI symptoms. Patients with only GI symptoms had less severe disease compared with those who had both GI and respiratory symptoms, who had more severe disease with higher mortality. Overall mortality was 8.6%, with no mortality in the GI symptoms alone group. The highest severity and mortality were in patients with both GI and respiratory symptoms (48.39 and 13.33%, respectively). Conclusions: COVID-19-related gastrointestinal symptoms are common, and their presence alone carries a better prognosis, but their presence with respiratory symptoms is associated with higher morbidity and mortality.