Dataset Information

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

ABSTRACT: COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.

SUBMITTER: Singh T

PROVIDER: S-EPMC9295614 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

Singh Tarishi T Saurabh Praneet P Bisen Dhananjay D Kane Lalit L Pathak Mayank M Sinha G R GR

Computational intelligence and neuroscience 20220705

COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to thi ...[more]

PMID: 35865493

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Project description:ObjectivesThe automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.Materials and methodsA corpus of 6170 tweets about COVID-19 vaccination, posted between March 1, 2020 and January 4, 2022, was manually annotated by native speakers. Traditional predictive models were compared with deep learning models to ascertain a baseline performance for the detection of stance in Spanish tweets. The evaluation focused on the ability of multilingual and language-specific embeddings to contextualize the topic of those short texts adequately.ResultsThe BERT-Multi+BiLSTM combination yielded the best results (macroaveraged F1 and Matthews correlation coefficient scores of 0.86 and 0.79, respectively; interpolated area under the receiver operating curve [AUC] of 0.95 for tweets against vaccination and 0.85 in favor of vaccination and a score of 0.97 for tweets containing no stance information), closely followed by the BETO+BiLSTM and RoBERTa BNE-LSTM Spanish models and the term frequency-inverse document frequency+SVM model (average AUC decrease of 0.01). The main differentiating factor among these models was the ability to predict tweets against vaccination.DiscussionThe BERT Multi+BILSTM model outperformed the other models in terms of per class prediction capacity. The main assumption is that language-specific embeddings do not outperform multilingual embeddings or TF-IDF features because of the context of the topic. The inherent context of BERT or RoBERTa embeddings is general. So, these embeddings are not familiar with the slang commonly used on Twitter and, more specifically, during the pandemic.ConclusionThe best performing model detects tweet stance with performance high enough to ensure its usefulness for public health applications, namely awareness campaigns, misinformation detection and other early intervention and prevention actions seeking to improve an individual's well-being based on autoreported experiences and opinions. The dataset and code of the study are available on GitHub.

Project description:The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3000 images, and most of these networks achieved a sensitivity rate of 98% ( ± 3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git.

Dataset Information

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

Publications

Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19.

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