Automated detection of COVID-19 cases using deep neural networks with X-ray images

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records

BACKGROUND

An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data.

RESULTS

We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77).

CONCLUSIONS

Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients.

COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy.

DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images

A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.

Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies

Background and Aims

One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19.

Methods

On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords.

Results

The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles.

Conclusion

The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients

Purpose

Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data.

Approach

We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity.

Results

Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs consistently better than predictive models developed using only imaging or non-imaging features, with area under the receiver operating characteristics curve of 0.76, 0.90, and 0.75 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from the Mayo Clinic. Our scheme highlights known biases in the model prediction, such as bias against patients with alcohol abuse history and bias based on insurance status.

Conclusions

Our study signifies the importance of the fusion of multiple data modalities for the accurate prediction of clinical trajectories. The proposed graph structure can model relationships between patients based on non-imaging EHR data, and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling

PROBLEM

Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia.

AIM

In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia.

METHODS

Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy.

RESULTS

On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters.

CONCLUSION

The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives

In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies.

COVID-SegNet: encoder-decoder-based architecture for COVID-19 lesion segmentation in chest X-ray

The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder-decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.

Pneumonia Detection Using Enhanced Convolutional Neural Network Model on Chest X-Ray Images

Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favored to recover infected patients since untreated pneumonia can lead to major complications in the elderly (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate big X-ray images (XRIs) of the chest, to determine whether the images show/do not show signs of pneumonia, and to compare the models based on their accuracy, precision, recall, loss, and receiver operating characteristic area under the ROC curve scores. Enhanced convolutional neural network (CNN), VGG-19, ResNet-50, and ResNet-50 with fine-tuning are some of the deep learning (DL) algorithms employed in this study. By training the transfer learning model and enhanced CNN model using a big data set, these techniques are used to identify pneumonia. The data set for the study was obtained from Kaggle. It should be noted that the data set has been expanded to include further records. This data set included 5863 chest XRIs, which were categorized into 3 different folders (i.e., train, val, test). These data are produced every day from personnel records and Internet of Medical Things devices. According to the experimental findings, the ResNet-50 model showed the lowest accuracy, that is, 82.8%, while the enhanced CNN model showed the highest accuracy of 92.4%. Owing to its high accuracy, enhanced CNN was regarded as the best model in this study. The techniques developed in this study outperformed the popular ensemble techniques, and the models showed better results than those generated by cutting-edge methods. Our study implication is that a DL models can detect the progression of pneumonia, which improves the general diagnostic accuracy and gives patients new hope for speedy treatment. Since enhanced CNN and ResNet-50 showed the highest accuracy compared with other algorithms, it was concluded that these techniques could be effectively used to identify pneumonia after performing fine-tuning.

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Semantically preserving adversarial unsupervised domain adaptation network for improving disease recognition from chest x-rays

Supervised deep learning methods have been successfully applied in medical imaging. However, training deep learning systems often requires ample annotated data. Due to cost and time restrictions, not all collected medical images, e.g., chest x-rays (CXRs), can be labeled in practice. To classify these unlabeled images, a solution may involve adopting a model trained with sufficient labeled data in relevant domains (with both source and target being CXRs). However, domain shift may cause the trained model not able to generalize well on unlabeled target datasets. This work aims to develop a novel unsupervised domain adaptation (UDA) framework to improve recognition performance on unlabeled target data. We present a semantically preserving adversarial UDA network, i.e., SPA-UDA net, with the potential to bridge the domain gap, by reconstructing the images in the target domain via an adversarial encode-and-reconstruct translation architecture. To preserve the class-specific semantic information (i.e., with or without disease) of the original images when translating, a semantically consistent framework is embedded. This framework is designed to guarantee that fine-grained disease-related information on the original images can be safely transferred. Furthermore, the proposed SPA-UDA net does not require paired images from source and target domains when training, which reduces the cost of arranging data significantly and is ideal for UDA. We evaluate the proposed SPA-UDA net on two public CXR datasets for lung disease recognition. The experimental results show that the proposed framework achieves significant performance improvements compared to other state-of-the-art UDA methods.

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.

A COVID-19 medical image classification algorithm based on Transformer

Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency.

An XAI approach for COVID-19 detection using transfer learning with X-ray images

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use and importance of Artificial Intelligence (AI) dramatically increasing. This paper proposes a model using an Explainable Artificial Intelligence (XAI) technique to detect and interpret COVID-19 positive CXR images. We further analyze the impact of COVID-19 positive CXR images using heatmaps. The proposed model leverages transfer learning and data augmentation techniques for faster and more adequate model training. Lung segmentation is applied to enhance the model performance further. We conducted a pre-trained network comparison with the highest classification performance (F1-Score: 98%) using the ResNet model.

COVID-19 detection based on self-supervised transfer learning using chest X-ray images

PURPOSE

Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation.

METHODS

In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection.

RESULTS

Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability.

CONCLUSIONS

Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The "MSID" dataset, short form of "Monkeypox Skin Images Dataset", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan’s daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model.

Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models

In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time.

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.

Review on chest pathogies detection systems using deep learning techniques

Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.

Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence

There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.

The design of a point of care FET biosensor to detect and screen COVID-19

Graphene field effect transistor (FET) biosensors have attracted huge attention in the point-of-care and accurate detection. With the recent spread of the new emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the need for rapid, and accurate detection & screening tools is arising. Employing these easy-to-handle sensors can offer cheap, rapid, and accurate detection. Herein, we propose the design of a reduced graphene oxide (rGO) FET biosensor for the detection of SARS-CoV-2. The main objective of this work is to detect the SARS-CoV-2 spike protein antigen on spot selectively and rapidly. The sensor consists of rGO channel, a pair of golden electrodes, and a gate underneath the channel. The channel is functionalized with COVID-19 spike protein antibodies to achieve selectivity, and with metal nanoparticles (MNPs) such as copper and silver to enhance the bio-sensing performance. The designed sensor successfully detects the SARS-CoV-2 spike protein and shows singular electrical behavior for detection. The semi-empirical modeling approach combined with none-equilibrium Green's function were used to study the electronic transport properties of the rGO-FET biosensor before and after the addition of the target molecules. The sensor's selectivity is also tested against other viruses. This study provides a promising guide for future practical fabrication.

Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review

This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.

Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier

In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train–test splits (70–30%, 80–20%, and 90–10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.

Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review

In today’s environment of the rapid rise of artificial intelligence (AI), debate continues about whether it has beneficial effects on economic development. However, there is only a fragmented perception of what role and place AI technology actually plays in economic development (ED). In this paper, we pioneer the research by focusing our detective work and discussion on the intersection of AI and economic development. Specifically, we adopt a two-step methodology. At the first step, we analyze 2211 documents in the AI&ED field using the bibliometric tool Bibliometrix, presenting the internal structure and external characteristics of the field through different metrics and algorithms. In the second step, a qualitative content analysis of clusters calculated from the bibliographic coupling algorithm is conducted, detailing the content directions of recently distributed topics in the AI&ED field from different perspectives. The results of the bibliometric analysis suggest that the number of publications in the field has grown exponentially in recent years, and the most relevant source is the “Sustainability” journal. In addition, deep learning and data mining-related research are the key directions for the future. On the whole, scholars dedicated to the field have developed close cooperation and communication across the board. On the other hand, the content analysis demonstrates that most of the research is centered on the five facets of intelligent decision-making, social governance, labor and capital, Industry 4.0, and innovation. The results provide a forward-looking guide for scholars to grasp the current state and potential knowledge gaps in the AI&ED field.

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

COVID-19 radiograph prognosis using a deep CResNeXt network

COVID-19 has caused an epidemic in the entire world and it is caused by the novel virus SARS-COV-2. In severe conditions, this virus can cause a critical lung infection or viral pneumonia. To administer the correct treatment to patients, COVID-19 testing is important for diagnosing and determining patients who are infected with COVID-19, as opposed to those infected with other bacterial or viral infections. In this paper, a CResNeXt chest radiograph COVID-19 prediction model is proposed using residual network architecture. The advantage of the proposed model is that it requires lesser free hyper-parameters as compared to other residual networks. In addition, the training time per epochs of the model is very less compared to VGG19, ResNet-50, ResNeXt. The proposed CResNeXt model's binary classification (COVID-19 versus No-Finding) accuracy is observed to be 98.63% and 99.99% and multi-class classification (COVID-19, Pneumonia, and No-Finding) accuracy is observed to be 97.42% and 99.27% on the original and augmented datasets, respectively.

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists

OBJECTIVE

To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity.

METHODS

We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method.

RESULTS

Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists.

CONCLUSION

This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Background and Objective

In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures.

Methods

Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes.

Results

PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases.

Conclusion

The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey

BACKGROUND

Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals.

METHODS

An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach.

RESULTS

A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation.

DISCUSSION

Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers.

CONCLUSION

Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.

Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

A deep learning approach to track Arabidopsis seedlings' circumnutation from time-lapse videos

BACKGROUND

Circumnutation (Darwin et al., Sci Rep 10(1):1-13, 2000) is the side-to-side movement common among growing plant appendages but the purpose of circumnutation is not always clear. Accurately tracking and quantifying circumnutation can help researchers to better study its underlying purpose.

RESULTS

In this paper, a deep learning-based model is proposed to track the circumnutating flowering apices in the plant Arabidopsis thaliana from time-lapse videos. By utilizing U-Net to segment the apex, and combining it with the model update mechanism, pre- and post- processing steps, the proposed model significantly improves the tracking time and accuracy over other baseline tracking methods. Additionally, we evaluate the computational complexity of the proposed model and further develop a method to accelerate the inference speed of the model. The fast algorithm can track the apices in real-time on a computer without a dedicated GPU.

CONCLUSION

We demonstrate that the accuracy of tracking the flowering apices in the plant Arabidopsis thaliana can be improved with our proposed deep learning-based model in terms of both the racking success rate and the tracking error. We also show that the improvement in the tracking accuracy is statistically significant. The time-lapse video dataset of Arabidopsis is also provided which can be used for future studies on Arabidopsis in various takes.

COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of large, well annotated datasets poses a challenge in developing effective deep neural networks, especially in the case of rare diseases and new pandemics. To address this issue, we present COVID-Net USPro, an explainable few-shot deep prototypical network that is designed to detect COVID-19 cases from very few ultrasound images. Through intensive quantitative and qualitative assessments, the network not only demonstrates high performance in identifying COVID-19 positive cases, using an explainability component, but it is also shown that the network makes decisions based on the actual representative patterns of the disease. Specifically, COVID-Net USPro achieves 99.55% overall accuracy, 99.93% recall, and 99.83% precision for COVID-19-positive cases when trained with only five shots. In addition to the quantitative performance assessment, our contributing clinician with extensive experience in POCUS interpretation verified the analytic pipeline and results, ensuring that the network's decisions are based on clinically relevant image patterns integral to COVID-19 diagnosis. We believe that network explainability and clinical validation are integral components for the successful adoption of deep learning in the medical field. As part of the COVID-Net initiative, and to promote reproducibility and foster further innovation, the network is open-sourced and available to the public.

Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma

When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.

CsPbBr3-DMSO merged perovskite micro-bricks for efficient X-ray detection

Inorganic perovskite wafers with good stability and adjustable sizes are promising in X-ray detection but the high synthetic temperature is a hindrance. Herein, dimethyl sulfoxide (DMSO) is used to prepare the CsPbBr3 micro-bricks powder at room temperature. The CsPbBr3 powder has a cubic shape with few crystal defects, small charge trap density, and high crystallinity. A trace amount of DMSO attaches to the surface of the CsPbBr3 micro-bricks via Pb-O bonding, forming the CsPbBr3-DMSO adduct. During hot isostatic processing, the released DMSO vapor merges the CsPbBr3 micro-bricks, producing a compact and dense CsPbBr3 wafer with minimized grain boundaries and excellent charge transport properties. The CsPbBr3 wafer shows a large mobility-lifetime (μτ) product of 5.16 × 10-4 cm2·V-1, high sensitivity of 14,430 μC·Gyair-1·cm-2, low detection limit of 564 nGyair·s-1, as well as robust stability in X-ray detection. The results reveal a novel strategy with immense practical potential pertaining to high-contrast X-ray detection.

Electronic Supplementary Material

Supplementary material (further details of the characterization, SEM images, AFM images, KPFM images, schematic illustration, XRD patterns, XPS spectra, FTIR spectra, UPS spectra, and stability tests) is available in the online version of this article at 10.1007/s12274-023-5487-3.

In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images

The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.

Digital Transformation and Open Innovation Planning of Response to COVID-19 Outbreak: A Systematic Literature Review and Future Research Agenda

The COVID-19 pandemic highlights the importance of digital technology in a specific region's epidemic prevention and control, and the digital transformation strategy based on the open innovation system is an emerging way to tackle conceivable outbreaks. Based on the bibliometric study of relevant literature data, this paper evaluated the research and development status in this field, and conducted a systematic literature review on the basis of the core articles identified. The results of bibliometric analysis software, including CiteSpace, CitNetExplorer and VOSViewer, showed that the development of relevant research presented rapidity and decentralization, and the evolution process of literature topics further implies the necessity of interdisciplinary and multisectoral collaboration. Furthermore, this paper summarized the specific implementation strategies for constructing an open innovation system, and discussed the role and development plan of digital technology in epidemic prevention and control.

DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.

Real-world evidence: Telemedicine for complicated cases of urinary tract infection

Telemedicine programs for the treatment of urinary tract infections (UTIs) offer an opportunity to reduce burdens on patients and providers. However, these programs are typically restricted to patients with uncomplicated UTIs. This real-world analysis evaluated treatment and resolution rates in a large-scale, national UTI telemedicine program inclusive of patients with uncomplicated and complicated UTIs. We conducted a retrospective analysis of data obtained from a commercially available telemedicine program for the treatment of UTIs among adult women in the US between 2017 and 2021 (n = 51,474). The primary outcomes were the number of women who presented with symptoms of uncomplicated UTI, complicated UTI, and vaginal infection; prescription use and antibiotic type; symptom resolution within seven days after appointment; and treatment failure or relapse. Most patients reported frequent urination (94.4%), urgency (94.5%), and dysuria (97.6%). Those with uncomplicated UTI symptoms represented the majority of patients (61.6%); however, a substantial number of patients (36.5%) also reported at least one symptom associated with a complicated UTI. One-fifth of patients (19.2%) reported at least one co-occurring symptom of vaginal infection or sexually transmitted infection. Across all treated patients, 94.0% received recommended antibiotics according to the clinical protocol. Of the treated patients who provided follow-up data (n = 3,521), 89.7% reported seven-day symptom resolution. Symptom resolution rates were similar between patients with uncomplicated UTI symptoms (90.8%) and complicated UTI symptoms (87.9%), and symptom resolution among all treated patients (89.7%) was similar to reports for in-person standard of care. These findings suggest that large-scale telemedicine programs for the treatment of UTIs can be effective in the treatment of complicated UTIs.

Utilisation of deep learning for COVID-19 diagnosis

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Background and Objective

The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods

The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results

Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion

Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation

BACKGROUND AND OBJECTIVE

Covid-19 infections are spreading around the globe since December 2019. Several diagnostic methods were developed based on biological investigations and the success of each method depends on the accuracy of identifying Covid infections. However, access to diagnostic tools can be limited, depending on geographic region and the diagnosis duration plays an important role in treating Covid-19. Since the virus causes pneumonia, its presence can also be detected using medical imaging by Radiologists. Hospitals with X-ray capabilities are widely distributed all over the world, so a method for diagnosing Covid-19 from chest X-rays would present itself. Studies have shown promising results in automatically detecting Covid-19 from medical images using supervised Artificial neural network (ANN) algorithms. The major drawback of supervised learning algorithms is that they require huge amounts of data to train. Also, the radiology equipment is not computationally efficient for deep neural networks. Therefore, we aim to develop a Generative Adversarial Network (GAN) based image augmentation to optimize the performance of custom, light, Convolutional networks used for the classification of Chest X-rays (CXR).

METHODS

A Progressively Growing Generative Adversarial Network (PGGAN) is used to generate synthetic and augmented data to supplement the dataset. We propose two novel CNN architectures to perform the Multi-class classification of Covid-19, healthy and pneumonia affected Chest X-rays. Comparisons have been drawn to the state of the art models and transfer learning methods to evaluate the superiority of the networks. All the models are trained using enhanced and augmented X-ray images and are compared based on classification metrics.

RESULTS

The proposed models had extremely high classification metrics with proposed Architectures having test accuracy of 98.78% and 99.2% respectively while having 40% lesser training parameters than their state of the art counterpart.

CONCLUSION

In the present study, a method based on artificial intelligence is proposed, leading to a rapid diagnostic tool for Covid infections based on Generative Adversarial Network (GAN) and Convolutional Neural Networks (CNN). The benefit will be a high accuracy of detection with up to 99% hit rate, a rapid diagnosis, and an accessible Covid identification method by chest X-ray images.

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

Objectives

When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.

Material and methods

The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.

Results

In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.

Conclusion

The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.