Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

COVID-19 at a Glance: An Up-to-Date Overview on Variants, Drug Design and Therapies

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronavirus family which caused the worldwide pandemic of human respiratory illness coronavirus disease 2019 (COVID-19). Presumably emerging at the end of 2019, it poses a severe threat to public health and safety, with a high incidence of transmission, predominately through aerosols and/or direct contact with infected surfaces. In 2020, the search for vaccines began, leading to the obtaining of, to date, about twenty COVID-19 vaccines approved for use in at least one country. However, COVID-19 continues to spread and new genetic mutations and variants have been discovered, requiring pharmacological treatments. The most common therapies for COVID-19 are represented by antiviral and antimalarial agents, antibiotics, immunomodulators, angiotensin II receptor blockers, bradykinin B2 receptor antagonists and corticosteroids. In addition, nutraceuticals, vitamins D and C, omega-3 fatty acids and probiotics are under study. Finally, drug repositioning, which concerns the investigation of existing drugs for new therapeutic target indications, has been widely proposed in the literature for COVID-19 therapies. Considering the importance of this ongoing global public health emergency, this review aims to offer a synthetic up-to-date overview regarding diagnoses, variants and vaccines for COVID-19, with particular attention paid to the adopted treatments.

Hybrid classification structures for automatic COVID-19 detection

This paper explores the issue of COVID-19 detection from X-ray images. X-ray images, in general, suffer from low quality and low resolution. That is why the detection of different diseases from X-ray images requires sophisticated algorithms. First of all, machine learning (ML) is adopted on the features extracted manually from the X-ray images. Twelve classifiers are compared for this task. Simulation results reveal the superiority of Gaussian process (GP) and random forest (RF) classifiers. To extend the feasibility of this study, we have modified the feature extraction strategy to give deep features. Four pre-trained models, namely ResNet50, ResNet101, Inception-v3 and InceptionResnet-v2 are adopted in this study. Simulation results prove that InceptionResnet-v2 and ResNet101 with GP classifier achieve the best performance. Moreover, transfer learning (TL) is also introduced in this paper to enhance the COVID-19 detection process. The selected classification hierarchy is also compared with a convolutional neural network (CNN) model built from scratch to prove its quality of classification. Simulation results prove that deep features and TL methods provide the best performance that reached 100% for accuracy.

Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review

OBJECTIVES

 To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work.

METHODS

 The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test.

FINDINGS

 Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified.

INTERPRETATION

 A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.

Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost

Introduction

In late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray images.

Methods

In this paper, a method for diagnosing coronavirus disease from X-ray images was developed. In this method, DenseNet169 Deep Neural Network (DNN) was used to extract the features of X-ray images taken from the patients’ chests. The extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm to perform the classification task.

Results

Evaluation of the proposed approach and its comparison with the methods presented in recent years revealed that this method was more accurate and faster than the existing ones and had an acceptable performance for detecting COVID-19 cases from X-ray images. The experiments showed 98.23% and 89.70% accuracy, 99.78% and 100% specificity, 92.08% and 95.20% sensitivity in two and three-class problems, respectively.

Conclusion

This study aimed to detect people with COVID-19, focusing on non-clinical approaches. The developed method could be employed as an initial detection tool to assist the radiologists in more accurate and faster diagnosing the disease.

Implication for practice

The proposed method's simple implementation, along with its acceptable accuracy, allows it to be used in COVID-19 diagnosis. Moreover, the gradient-based class activation mapping (Grad-CAM) can be used to represent the deep neural network's decision area on a heatmap. Radiologists might use this heatmap to evaluate the chest area more accurately.

Detection and diagnosis of COVID-19 infection in lungs images using deep learning techniques

World's science and technologies have been challenged by the COVID-19 pandemic. Each and every community across the globe are trying to find a real-time novel method for accurate treatment and cure of COVID-19 infected patients. The most important lead to take from this pandemic is to detect the infected patients as soon as possible and provide them an accurate treatment. At present, the worldwide methodology to detect COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR). This technique is costly and time taking. For this reason, the implementation of a novel method is required. This paper includes the use of deep learning analysis to develop a system for identifying COVID-19 patients. Proposed technique is based on convolution neural network (CNN) and deep neural network (DNN). This paper proposes two models, first is designing DNN on the basis of fractal feature of the images and second is designing CNN using lungs x-ray images. To find the infected area (tissues) of the lungs image using CNN architecture, segmentation process has been used. Developed CNN architecture gave results of classification with accuracy equal to 94.6% and sensitivity equal to 90.5% which is much better than the proposed DNN method, which gave accuracy 84.11% and sensitivity 84.7%. The outcome of the presented model shows 94.6% accuracy in detecting infected regions. Using this method the growth of the infected regions can be monitored and controlled. The designed model can also be used in post-COVID-19 analysis.

A Decision-Level Fusion Method for COVID-19 Patient Health Prediction

With the continuous attempts to develop effective machine learning methods, information fusion approaches play an important role in integrating data from multiple sources and improving these methods' performance. Among the different fusion techniques, decision-level fusion has unique advantages to fuse the decisions of various classifiers and getting an effective outcome. In this paper, we propose a decision-level fusion method that combines three well-calibrated ensemble classifiers, namely, a random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) methods. It is used to predict the COVID-19 patient health for early monitoring and efficient treatment. A soft voting technique is used to generate the final decision result from the predictions of these calibrated classifiers. The method uses the COVID-19 patient's health information, travel demographic, and geographical data to predict the possible outcome of the COVID-19 case, recovered, or death. A different set of experiments is conducted on a public novel Corona Virus 2019 dataset using a different ratio of test sets. The experimental results show that the proposed fusion method achieved an accuracy of 97.24% and an F1-score of 0.97, which is higher than the current related work that has an accuracy of 94% and an F1-score 0.86, on 20% test set taken from the dataset.

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.

Exploiting probability density function of deep convolutional autoencoders' latent space for reliable COVID-19 detection on CT scans

We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach's performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study

Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. The epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global economies. This study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. It gives a detailed discussion on the existing COVID-19 detection methodologies (diagnosis, prognosis, and severity/risk detection) and the challenges encountered for the same. It also highlights the various preprocessing and post-processing methods involved to enhance the detection mechanism. This work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. Despite deep learning methods presenting high levels of efficiency, some limitations have been briefly described in the study. Hence, this review can help understand the utilization and pros and cons of deep learning in analyzing medical images.

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network

Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.

A cad system design based on HybridMultiscale convolutional Mantaray network for pneumonia diagnosis

Pneumonia is one of the diseases that people may encounter in any period of their lives. Recently, researches and developers all around the world are focussing on deep learning and image processing strategies to quicken the pneumonia diagnosis as those strategies are capable of processing numerous X-ray and computed tomography (CT) images. Clinicians need more time and appropriate experiences for making a diagnosis. Hence, a precise, reckless, and less expensive tool to detect pneumonia is necessary. Thus, this research focuses on classifying the pneumonia chest X-ray images by proposing a very efficient stacked approach to improve the image quality and hybridmultiscale convolutional mantaray feature extraction network model with high accuracy. The input dataset is restructured with the sake of a hybrid fuzzy colored and stacking approach. Then the deep feature extraction stage is processed with the aid of stacking dataset by hybrid multiscale feature extraction unit to extract multiple features. Also, the features and network size are diminished by the self-attention module (SAM) based convolutional neural network (CNN). In addition to this, the error in the proposed network model will get reduced with the aid of adaptivemantaray foraging optimization (AMRFO) approach. Finally, the support vector regression (SVR) is suggested to classify the presence of pneumonia. The proposed module has been compared with existing technique to prove the overall efficiency of the system. The huge collection of chest X-ray images from the kaggle dataset was emphasized to validate the proposed work. The experimental results reveal an outstanding performance of accuracy (97%), precision (95%) and f-score (96%) progressively.

COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network

Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database

Introduction

A prompt severity assessment model of patients with confirmed infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center on the basis of past treatment data of other patients with similar severity levels.

Methods

This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2-infected patients. The proposed model is trained on a nationwide data set provided by a Korean government agency and only requires patients’ basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The data set was collected from all Korean citizens with confirmed COVID-19 between February 2020 and July 2021 (N = 149,471).

Results

The experiments achieved high model performance with an approximate precision of 0.923 and area under the curve of receiver operating characteristic (AUROC) score of 0.950 [95% tolerance interval (TI) 0.940–0.958, 95% confidence interval (CI) 0.949–0.950]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis.

Conclusion

A prompt severity assessment model for managing infectious people has been attained through using a nationwide data set. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model’s high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications.

A cognitive framework based on deep neural network for classification of coronavirus disease

Since December 2019, the pandemic of coronavirus (CorV) is spreading all over the world. CorV is a viral disease that results in ill effects on humans and is recognized as public health concern globally. The objective of the paper is to diagnose and prevent the spread of CorV. Spatio-temporal based fine-tuned deep learning model is used for detecting Corv disease so that the prevention measures could be taken on time. Deep learning is an emerging technique that has an extensive approach to prediction. The proposed system presents a hybrid model using chest X-ray images to early identify the CorV suspected people so that necessary action can be taken timely. The proposed work consists of various deep learning neural network algorithms for the identification of CorV patients. A decision model with enhanced accuracy has been presented for early identification of the suspected CorV patients and time-sensitive decision-making. A SQueezeNet model is used for the classification of the CorV patient. An experiment has been conducted for validation purposes to register an average accuracy of 97.8%. Moreover, the outcomes of statistical parameters are compared with numerous state-of-the-art decision-making models in the current domain for performance assessment.

ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings

Rapid diagnosis of COVID-19 with high reliability is essential in the early stages. To this end, recent research often uses medical imaging combined with machine vision methods to diagnose COVID-19. However, the scarcity of medical images and the inherent differences in existing datasets that arise from different medical imaging tools, methods, and specialists may affect the generalization of machine learning-based methods. Also, most of these methods are trained and tested on the same dataset, reducing the generalizability and causing low reliability of the obtained model in real-world applications. This paper introduces an adversarial deep domain adaptation-based approach for diagnosing COVID-19 from lung CT scan images, termed ADA-COVID. Domain adaptation-based training process receives multiple datasets with different input domains to generate domain-invariant representations for medical images. Also, due to the excessive structural similarity of medical images compared to other image data in machine vision tasks, we use the triplet loss function to generate similar representations for samples of the same class (infected cases). The performance of ADA-COVID is evaluated and compared with other state-of-the-art COVID-19 diagnosis algorithms. The obtained results indicate that ADA-COVID achieves classification improvements of at least 3%, 20%, 20%, and 11% in accuracy, precision, recall, and F1 score, respectively, compared to the best results of competitors, even without directly training on the same data. The implementation source code of the ADA-COVID is publicly available at https://github.com/MehradAria/ADA-COVID.

Automated detection of COVID-19 through convolutional neural network using chest x-ray images

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Detection and Spatial Correlation Analysis of Infectious Diseases Using Wireless Body Area Network Under Imperfect Wireless Channel

The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients.

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.

A coarse-refine segmentation network for COVID-19 CT images

The rapid spread of the novel coronavirus disease 2019 (COVID-19) causes a significant impact on public health. It is critical to diagnose COVID-19 patients so that they can receive reasonable treatments quickly. The doctors can obtain a precise estimate of the infection's progression and decide more effective treatment options by segmenting the CT images of COVID-19 patients. However, it is challenging to segment infected regions in CT slices because the infected regions are multi-scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse-refine segmentation network is proposed to address these challenges. The coarse-refine architecture and hybrid loss is used to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. The atrous spatial pyramid pooling module in the network is added to improve the performance in detecting infected regions with different scales. Experimental results show that the model in the segmentation of COVID-19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options.

Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual's health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method's precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.

Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.

Classifier Fusion for Detection of COVID-19 from CT Scans

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

The effect of deep feature concatenation in the classification problem: An approach on COVID-19 disease detection

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet-B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: "COVID-19 Image Dataset," "COVID-19 Pneumonia Normal Chest X-ray (PA) Dataset," and "COVID-19 Radiography Database" for COVID-19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1-score values, as well. Contribution of paper is as follows: COVID-19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer-aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

A review of explainable and interpretable AI with applications in COVID-19 imaging

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks, including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to COVID-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID-19 to quickly understand the basics of several explainable AI techniques and assist in the selection of an approach that is both appropriate and effective for a given scenario.

Detection of novel coronavirus from chest X-rays using deep convolutional neural networks

With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast diagnostic system keeps on surging. With shortage of kits, and deadly underlying disease due to its vastly mutating and contagious properties, the tired physicians need a fast diagnostic method to cater the requirements of the soaring number of infected patients. Laboratory testing has turned out to be an arduous, cost-ineffective and requiring a well-equipped laboratory for analysis. This paper proposes a convolutional neural network (CNN) based model for analysis/detection of COVID-19, dubbed as CovCNN, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to assist the medical practitioners to expedite the diagnostic process amongst high workload conditions. In the proposed CovCNN model, a novel deep-CNN based architecture has been incorporated with multiple folds of CNN. These models utilize depth wise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. 657 chest X-rays of which 219 were X-ray images of patients infected from COVID-19 and the remaining were the images of non-COVID-19 (i.e. normal or COVID-19 negative) patients. Further, performance evaluation on the dataset using different pre-trained models has been analyzed based on the loss and accuracy curve. The experimental results show that the highest classification accuracy (98.4%) is achieved using the proposed CovCNN model.

External validation of 4c ISARIC mortality score in critically ill COVID-19 patients from Saudi Arabia

Background:

ISARIC mortality score is a risk stratification tool that helps predict the in-hospital mortality of COVID-19 patients. However, this tool was developed and validated in a British population, and thus, the external validation of this tool in local populations is important.

Objectives:

External validation of the ISARIC mortality score in COVID-19 patients from a large Saudi Arabian intensive care unit (ICU).

Methods:

This is a retrospective study that included all adult patients with COVID-19 admitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, from March 2020 to June 2021. Patients who were pregnant or had pulmonary tuberculosis/human immunodeficiency virus were excluded along with patients with missing variables. Data were collected to calculate the ISARIC mortality score and then fitting receiver operator characteristic curve against patients’ outcome.

Results:

A total of 1493 critically ill COVID-19 patients were included. The mortality was 38%, the area under the curve of the score was 0.81 (95% confidence interval [CI]: 0.79–0.83, P < 0.001) and the cutoff value correctly classified 72.7% of the cohort. The cutoff value of >9 had sensitivity of 70.5% (95% CI: 66.6–74.3); specificity, 73.97% (95% CI: 71–76.8); positive predictive value, 62.4% (95% CI: 59.5–65.2) and negative predictive value, 80.2% (95% CI: 78.2–82.4).

Conclusion:

The ISARIC score was found to have excellent predictive ability for mortality in critically ill COVID-19 patients in our Saudi Arabian cohort. A cutoff score of >9 was the optimal criterion.

COVID-19 classification using deep feature concatenation technique

Detecting COVID-19 from medical images is a challenging task that has excited scientists around the world. COVID-19 started in China in 2019, and it is still spreading even now. Chest X-ray and Computed Tomography (CT) scan are the most important imaging techniques for diagnosing COVID-19. All researchers are looking for effective solutions and fast treatment methods for this epidemic. To reduce the need for medical experts, fast and accurate automated detection techniques are introduced. Deep learning convolution neural network (DL-CNN) technologies are showing remarkable results for detecting cases of COVID-19. In this paper, deep feature concatenation (DFC) mechanism is utilized in two different ways. In the first one, DFC links deep features extracted from X-ray and CT scan using a simple proposed CNN. The other way depends on DFC to combine features extracted from either X-ray or CT scan using the proposed CNN architecture and two modern pre-trained CNNs: ResNet and GoogleNet. The DFC mechanism is applied to form a definitive classification descriptor. The proposed CNN architecture consists of three deep layers to overcome the problem of large time consumption. For each image type, the proposed CNN performance is studied using different optimization algorithms and different values for the maximum number of epochs, the learning rate (LR), and mini-batch (M-B) size. Experiments have demonstrated the superiority of the proposed approach compared to other modern and state-of-the-art methodologies in terms of accuracy, precision, recall and f_score.

Predicting pattern of coronavirus using X-ray and CT scan images

Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world's central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.

Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases. The framework utilizes a multi-access edge computing technology such that end-user can access available resources as well the CNN on the cloud. Early detection of COVID-19 can improve treatment and mitigate transmission. During peaks of infection, hospitals worldwide have suffered from heavy patient loads, bed shortages, inadequate testing kits and short-staffing problems. Due to the time-consuming nature of the standard RT-PCR test, the lack of expert radiologists, and evaluation issues relating to poor quality images, patients with severe conditions are sometimes unable to receive timely treatment. It is thus recommended to incorporate computational intelligence methodologies, which provides highly accurate detection in a matter of minutes, alongside traditional testing as an emergency measure. CNN has achieved extraordinary performance in numerous computational intelligence tasks. However, finding a systematic, automatic and optimal set of hyperparameters for building an efficient CNN for complex tasks remains challenging. Moreover, due to advancement of technology, data are collected at sparse location and hence accumulation of data from such a diverse sparse location poses a challenge. In this article, we propose a framework of computational intelligence-based algorithm that utilize the recent 5G mobile technology of multi-access edge computing along with a new CNN-model for automatic COVID-19 detection using raw chest X-ray images. This algorithm suggests that anyone having a 5G device (e.g., 5G mobile phone) should be able to use the CNN-based automatic COVID-19 detection tool. As part of the proposed automated model, the model introduces a novel CNN structure with the genetic algorithm (GA) for hyperparameter tuning. One such combination of GA and CNN is new in the application of COVID-19 detection/classification. The experimental results show that the developed framework could classify COVID-19 X-ray images with 98.48% accuracy which is higher than any of the performances achieved by other studies.

COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach

Due to this epidemic of COVID-19, the everyday lives, welfare, and wealth of a country are affected. Inefficiency, a lack of medical diagnostics, and inadequately trained healthcare professionals are among the most significant barriers to arresting the development of this disease. Blockchain offers enormous promise for providing consistent and reliable real time and smart health facilities offsite. The infected patients with COVID-19 have shown they often have a lung infection upon arrival. It can be detected and analyzed using CT scan images. Unfortunately, though, it is time-consuming and liable to error. Thus, the assessment of chest CT scans must be automated. The proposed method uses transfer deep learning techniques to analyze CT scan images automatically. Transfer deep learning can improve the parameters of networks on huge databases, and pretrained networks can be used effectively on small datasets. We proposed a model built on VGGNet19, a convolutional neural network to classify individuals infected with coronavirus utilizing images of CT radiographs. We have used a globally accessible CT scan database that included 2500 CT pictures with COVID-19 infection and 2500 CT images without COVID-19 infection. An extensive experiment has been conducted using three deep learning methods such as VGG19, Xception Net, and CNN. Experiment findings indicate that the proposed model outperforms the other Xception Net and CNN models considerably. The results demonstrate that the proposed models have an accuracy of up to 95% and area under the receiver operating characteristic curve up to 95%.

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection

BACKGROUND

Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models.

OBJECTIVE

This study aims to classify COVID-19 and normal patients from CXR images using semantic segmentation networks for detecting and labeling COVID-19 infected lung lobes in CXR images.

METHODS

For semantically segmenting infected lung lobes in CXR images for COVID-19 early detection, three structurally different deep learning (DL) networks such as SegNet, U-Net and hybrid CNN with SegNet plus U-Net, are proposed and investigated. Further, the optimized CXR image semantic segmentation networks such as GWO SegNet, GWO U-Net, and GWO hybrid CNN are developed with the grey wolf optimization (GWO) algorithm. The proposed DL networks are trained, tested, and validated without and with optimization on the openly available dataset that contains 2,572 COVID-19 CXR images including 2,174 training images and 398 testing images. The DL networks and their GWO optimized networks are also compared with other state-of-the-art models used to detect COVID-19 CXR images.

RESULTS

All optimized CXR image semantic segmentation networks for COVID-19 image detection developed in this study achieved detection accuracy higher than 92%. The result shows the superiority of optimized SegNet in segmenting COVID-19 infected lung lobes and classifying with an accuracy of 98.08% compared to optimized U-Net and hybrid CNN.

CONCLUSION

The optimized DL networks has potential to be utilised to more objectively and accurately identify COVID-19 disease using semantic segmentation of COVID-19 CXR images of the lungs.

Weakly-supervised lesion analysis with a CNN-based framework for COVID-19

Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

Automatic Sequence-Based Network for Lung Diseases Detection in Chest CT

Objective

To develop an accurate and rapid computed tomography (CT)-based interpretable AI system for the diagnosis of lung diseases.

Background

Most existing AI systems only focus on viral pneumonia (e.g., COVID-19), specifically, ignoring other similar lung diseases: e.g., bacterial pneumonia (BP), which should also be detected during CT screening. In this paper, we propose a unified sequence-based pneumonia classification network, called SLP-Net, which utilizes consecutiveness information for the differential diagnosis of viral pneumonia (VP), BP, and normal control cases from chest CT volumes.

Methods

Considering consecutive images of a CT volume as a time sequence input, compared with previous 2D slice-based or 3D volume-based methods, our SLP-Net can effectively use the spatial information and does not need a large amount of training data to avoid overfitting. Specifically, sequential convolutional neural networks (CNNs) with multi-scale receptive fields are first utilized to extract a set of higher-level representations, which are then fed into a convolutional long short-term memory (ConvLSTM) module to construct axial dimensional feature maps. A novel adaptive-weighted cross-entropy loss (ACE) is introduced to optimize the output of the SLP-Net with a view to ensuring that as many valid features from the previous images as possible are encoded into the later CT image. In addition, we employ sequence attention maps for auxiliary classification to enhance the confidence level of the results and produce a case-level prediction.

Results

For evaluation, we constructed a dataset of 258 chest CT volumes with 153 VP, 42 BP, and 63 normal control cases, for a total of 43,421 slices. We implemented a comprehensive comparison between our SLP-Net and several state-of-the-art methods across the dataset. Our proposed method obtained significant performance without a large amount of data, outperformed other slice-based and volume-based approaches. The superior evaluation performance achieved in the classification experiments demonstrated the ability of our model in the differential diagnosis of VP, BP and normal cases.

Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT

BACKGROUND

Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important.

METHOD

In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data.

RESULTS

The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy.

CONCLUSION

Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase-Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model's practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.

A novel Gray-Scale spatial exploitation learning Net for COVID-19 by crawling Internet resources

Today, the earth planet suffers from the decay of active pandemic COVID-19 which motivates scientists and researchers to detect and diagnose the infected people. Chest X-ray (CXR) image is a common utility tool for detection. Even the CXR suffers from low informative details about COVID-19 patches; the computer vision helps to overcome it through grayscale spatial exploitation analysis. In turn, it is highly recommended to acquire more CXR images to increase the capacity and ability to learn for mining the grayscale spatial exploitation. In this paper, an efficient Gray-scale Spatial Exploitation Net (GSEN) is designed by employing web pages crawling across cloud computing environments. The motivation of this work are i) utilizing a framework methodology for constructing consistent dataset by web crawling to update the dataset continuously per crawling iteration; ii) designing lightweight, fast learning, comparable accuracy, and fine-tuned parameters gray-scale spatial exploitation deep neural net; iii) comprehensive evaluation of the designed gray-scale spatial exploitation net for different collected dataset(s) based on web COVID-19 crawling verse the transfer learning of the pre-trained nets. Different experiments have been performed for benchmarking both the proposed web crawling framework methodology and the designed gray-scale spatial exploitation net. Due to the accuracy metric, the proposed net achieves 95.60% for two-class labels, and 92.67% for three-class labels, respectively compared with the most recent transfer learning Google-Net, VGG-19, Res-Net 50, and Alex-Net approaches. Furthermore, web crawling utilizes the accuracy rates improvement in a positive relationship to the cardinality of crawled CXR dataset.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.