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1.
Technol Forecast Soc Change ; 183: 121911, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-1967167

ABSTRACT

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

2.
Knowl Based Syst ; : 109539, 2022 Jul 28.
Article in English | MEDLINE | ID: covidwho-1966919

ABSTRACT

Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a large-scale, and with real time answers. A novel approach for COVID-19 assessment is adopted here, discriminating negative subjects versus positive or recovered subjects. The scope is to identify potential discriminating features, highlight mid and short-term effects of COVID on the voice and compare two custom algorithms. A pool of 310 subjects took part in the study; recordings were collected in a low-noise, controlled setting employing three different vocal tasks. Binary classifications followed, using two different custom algorithms. The first was based on the coupling of boosting and bagging, with an AdaBoost classifier using Random Forest learners. A feature selection process was employed for the training, identifying a subset of features acting as clinically relevant biomarkers. The other approach was centred on two custom CNN architectures applied to mel-Spectrograms, with a custom knowledge-based data augmentation. Performances, evaluated on an independent test set, were comparable: Adaboost and CNN differentiated COVID-19 positive from negative with accuracies of 100% and 95% respectively, and recovered from negative individuals with accuracies of 86.1% and 75% respectively. This study highlights the possibility to identify COVID-19 positive subjects, foreseeing a tool for on-site screening, while also considering recovered subjects and the effects of COVID-19 on the voice. The two proposed novel architectures allow for the identification of biomarkers and demonstrate the ongoing relevance of traditional ML versus deep learning in speech analysis.

3.
Signal Process Image Commun ; : 116835, 2022 Aug 02.
Article in English | MEDLINE | ID: covidwho-1966640

ABSTRACT

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

4.
Comput Electr Eng ; : 108274, 2022 Aug 03.
Article in English | MEDLINE | ID: covidwho-1966457

ABSTRACT

Over the last two years, the novel coronavirus has become a significant threat to the health of the public, and numerous approaches are developed to determine the symptoms of COVID-19. To deal with the complex symptoms of COVID-19, a Deep Learning-assisted Multi-modal Data Analysis (DMDA) approach is introduced to determine COVID-19 symptoms by utilizing acoustic and image-based data. Furthermore, the classified events are forwarded to the proposed Dynamic Fusion Strategy (DFS) for confirming the health status of the individual. Initially, the performance of the proposed solution is evaluated on both acoustic and image-based samples and the proposed solution attains the maximum accuracy of 96.88% and 98.76%, respectively. Similarly, the DFS has achieved an overall symptom determination accuracy of 98.72% which is highly acceptable for decision-making. Moreover, the proposed solution shows high reliability with an accuracy of 95.64% even in absence of any one of the data modalities during testing.

5.
Appl Soft Comput ; : 109401, 2022 Jul 29.
Article in English | MEDLINE | ID: covidwho-1966354

ABSTRACT

The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral-temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral-temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral-temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral-temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral-temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral-temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.

6.
Soc Netw Anal Min ; 12(1): 94, 2022.
Article in English | MEDLINE | ID: covidwho-1966192

ABSTRACT

The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.

7.
Multimed Tools Appl ; : 1-42, 2022 Aug 01.
Article in English | MEDLINE | ID: covidwho-1966164

ABSTRACT

The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results.

8.
Multimed Tools Appl ; : 1-20, 2022 Jul 28.
Article in English | MEDLINE | ID: covidwho-1966163

ABSTRACT

Research aimed at finding solutions to the problem of the diffusion of distinct forms of non-genuine information online across multiple domains has attracted growing interest in recent years, from opinion spam to fake news detection. Currently, partly due to the COVID-19 virus outbreak and the subsequent proliferation of unfounded claims and highly biased content, attention has focused on developing solutions that can automatically assess the genuineness of health information. Most of these approaches, applied both to Web pages and social media content, rely primarily on the use of handcrafted features in conjunction with Machine Learning. In this article, instead, we propose a health misinformation detection model that exploits as features the embedded representations of some structural and content characteristics of Web pages, which are obtained using an embedding model pre-trained on medical data. Such features are employed within a deep learning classification model, which categorizes genuine health information versus health misinformation. The purpose of this article is therefore to evaluate the effectiveness of the proposed model, namely Vec4Cred, with respect to the problem considered. This model represents an evolution of a previous one, with respect to which new features and architectural choices have been considered and illustrated in this work.

9.
Concurrency and Computation: Practice and Experience ; n/a(n/a):e7157, 2022.
Article in English | Wiley | ID: covidwho-1966036

ABSTRACT

Summary The corona virus disease 2019 (COVID-19) pandemic has a severe influence on population health all over the world. Various methods are developed for detecting the COVID-19, but the process of diagnosing this problem from radiology and radiography images is one of the effective procedures for diagnosing the affected patients. Therefore, a robust and effective multi-local texture features (MLTF)-based feature extraction approach and Improved Weed Sea-based DeepNet (IWS-based DeepNet) approach is proposed for detecting the COVID-19 at an earlier stage. The developed IWS-based DeepNet is developed for detecting COVID-19to optimize the structure of the Deep Convolutional Neural Network (Deep CNN). The IWS is devised by incorporating the Improved Invasive Weed Optimization (IIWO) and Sea Lion Optimization (SLnO), respectively. The noises present in the input chest x-ray (CXR) image are discarded using Region of Interest (RoI) extraction by adaptive thresholding technique. For feature extraction, the proposed MLFT is newly developed by considering various texture features for extracting the best features. Finally, the COVID-19 detection is performed using the proposed IWS-based DeepNet. Furthermore, the proposed technique achieved effective performance in terms of True Positive Rate (TPR), True Negative Rate (TNR), and accuracy with the maximum values of 0.933%, 0.890%, and 0.919%.

10.
Front Public Health ; 10: 886958, 2022.
Article in English | MEDLINE | ID: covidwho-1963620

ABSTRACT

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.


Subject(s)
COVID-19 , Attention , Humans
11.
Computer Science Journal of Moldova ; 30(2):214-222, 2022.
Article in English | Scopus | ID: covidwho-1965236

ABSTRACT

The Coronavirus Pandemic triggered by SARS-CoV-2 has wreaked havoc on the planet and is expanding exponentially. While scanning methods, including CT scans and chest X-rays, are commonly used, artificial intelligence implementations are also deployed for COVID-based pneumonia detection. Due to image biases in X-ray data, bilateral filtration and Histogram Equalization are used followed by lung segmentation by a U-Net, which successfully segmented 83.2% of the collected dataset. The segmented lungs are fed into a Quadruplet Network with SqueezeNet encoders for increased computational efficiency and high-level embeddings generation. The embeddings are computed using a Multi-Layer Perceptron and visualized by T-SNE (T-Distributed Stochastic Neighbor Embedding) scatterplots. The proposed research results in a 94.6% classifying accuracy which is 2% more than the baseline Convolutional Neural Network and a 90.2% decrease in prediction time. © 2022 by CSJM;Pranshav Gajjar, Naishadh Mehta, Pooja Shah

12.
Intelligent Automation and Soft Computing ; 35(2):2383-2398, 2023.
Article in English | Scopus | ID: covidwho-1965089

ABSTRACT

Internet of things (IoT) has brought a greater transformation in health-care sector thereby improving patient care, minimizing treatment costs. The pre-sent method employs classical mechanisms for extracting features and a regression model for prediction. These methods have failed to consider the pollution aspects involved during COVID 19 prediction. Utilizing Ensemble Deep Learning and Framingham Feature Extraction (FFE) techniques, a smart health-care system is introduced for COVID-19 pandemic disease diagnosis. The Col-lected feature or data via predictive mechanisms to form pollution maps. Those maps are used to implement real-time countermeasures, such as storing the extracted data or feature in a Cloud server to minimize concentrations of air pol-lutants. Once integrated with patient management systems, this solution would minimize pollution emitted via patient’s sensors by offering spaces in the cloud server when pollution thresholds are reached. Second, the Gini Index factor information gain technique eliminates unimportant and redundant attributes while selecting the most relevant, reducing computing overhead and optimizing system performance. Finally, the COVID-19 disease prognosis ensemble deep learning-based classifier is constructed. Experimental analysis is planned to measure the prediction accuracy, error, precision and recall for different numbers of patients. Experimental results show that prediction accuracy is improved by 8%, error rate was reduced by 47% and prediction time is minimized by 36% compared to existing methods. © 2023, Tech Science Press. All rights reserved.

13.
Intelligent Automation and Soft Computing ; 35(2):1721-1736, 2023.
Article in English | Scopus | ID: covidwho-1965088

ABSTRACT

Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situa-tion: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. How-ever, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specia-list in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%. © 2023, Tech Science Press. All rights reserved.

14.
International Journal of Advanced Technology and Engineering Exploration ; 9(90):623-643, 2022.
Article in English | ProQuest Central | ID: covidwho-1964885

ABSTRACT

A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.

15.
Quantitative Biology ; 10(2):125-138, 2022.
Article in English | Scopus | ID: covidwho-1964759

ABSTRACT

Background: Modern machine learning-based models have not been harnessed to their total capacity for disease trend predictions prior to the COVID-19 pandemic. This work is the first use of the conditional RNN model in predicting disease trends that we know of during development that complemented classical epidemiological approaches. Methods: We developed the long short-term memory networks with quantile output (condLSTM-Q) model for making quantile predictions on COVID-19 death tolls. Results: We verified that the condLSTM-Q was accurately predicting fine-scale, county-level daily deaths with a two-week window. The model’s performance was robust and comparable to, if not slightly better than well-known, publicly available models. This provides unique opportunities for investigating trends within the states and interactions between counties along state borders. In addition, by analyzing the importance of the categorical data, one could learn which features are risk factors that affect the death trend and provide handles for officials to ameliorate the risks. Conclusion: The condLSTM-Q model performed robustly, provided fine-scale, county-level predictions of daily deaths with a two-week window. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease and could be further developed to generate other valuable predictions such as new cases or hospitalizations intuitively. © The Author (s) 2022. Published by Higher Education Press.

16.
Journal of Geodesy and Geoinformation Science ; 5(2):38-48, 2022.
Article in English | ProQuest Central | ID: covidwho-1964617

ABSTRACT

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

17.
AIMS Biophysics ; 8(4):346-371, 2021.
Article in English | Scopus | ID: covidwho-1964164

ABSTRACT

The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

18.
Data ; 7(7):95, 2022.
Article in English | ProQuest Central | ID: covidwho-1963771

ABSTRACT

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine-region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People’s Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state of the art for image segmentation methods toward improving the performance of the fine-grained segmentation of TB-consistent findings in digital chest X-ray images. The annotation collection comprises the following: (1) annotation files in JavaScript Object Notation (JSON) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients;(2) mask files saved in PNG format for each abnormality per TB patient;and (3) a comma-separated values (CSV) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs.Dataset:https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.Dataset License: Attribution 4.0 International—CC BY-4.0

19.
Atmosphere ; 13(7):1023, 2022.
Article in English | ProQuest Central | ID: covidwho-1963692

ABSTRACT

(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points’ address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale;furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.

20.
Applied Sciences ; 12(14):6925, 2022.
Article in English | ProQuest Central | ID: covidwho-1963682

ABSTRACT

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

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