CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.

Federated learning for COVID-19 screening from Chest X-ray images

Computers and Electrical Engineering

https://doi.org/10.1016/j.talanta.2022.123409

Review of COVID-19 testing and diagnostic methods

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray.

CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients

Recent decades have witnessed rapid development in the field of medical image segmentation. Deep learning-based fully convolution neural networks have played a significant role in the development of automated medical image segmentation models. Though immensely effective, such networks only take into account localized features and are unable to capitalize on the global context of medical image. In this paper, two deep learning based models have been proposed namely USegTransformer-P and USegTransformer-S. The proposed models capitalize upon local features and global features by amalgamating the transformer-based encoders and convolution-based encoders to segment medical images with high precision. Both the proposed models deliver promising results, performing better than the previous state of the art models in various segmentation tasks such as Brain tumor, Lung nodules, Skin lesion and Nuclei segmentation. The authors believe that the ability of USegTransformer-P and USegTransformer-S to perform segmentation with high precision could remarkably benefit medical practitioners and radiologists around the world. 

/pdf/semantic-segmentation-in-medical-images-through-transfused-2046sm38.pdf

Semantic segmentation in medical images through transfused convolution and transformer networks

False or misleading information, created deliberately to misinform and deceive the reader, is referred to as Fake News. As the most extensively utilized network for disseminating information, social media has become a highly popular platform for individuals to propagate incorrect information. Fake news can be pernicious when it comes to specific individuals and their opinions and perceptions, influencing and misleading their actions. Several initiatives have been taken in the past to reduce the spread of fake news and detect inaccurate information by devoting a significant amount of time and effort. In this paper, we have compared the performance of various Machine Learning and Deep Learning models for Fake News Detection. For our study, we used four datasets. From our experimentation, we realized that Random Forest and Bag of Words on the FARN Dataset outperformed the rest with an accuracy of 98.8%. In addition, we discovered that TF-IDF outperforms other feature extraction methods.

Study of Fake News Detection using Machine Learning and Deep Learning Classification Methods

The amount of text data available online is increasing at a very fast pace hence text summarization has become essential. Most of the modern recommender and text classification systems require going through a huge amount of data. Manually generating precise and fluent summaries of lengthy articles is a very tiresome and time-consuming task. Hence generating automated summaries for the data and using it to train machine learning models will make these models space and time-efficient. Extractive summarization and abstractive summarization are two separate methods of generating summaries. The extractive technique identifies the relevant sentences from the original document and extracts only those from the text. Whereas in abstractive summarization techniques, the summary is generated after interpreting the original text, hence making it more complicated. In this paper, we will be presenting a comprehensive comparison of a few transformer architecture based pre-trained models for text summarization. For analysis and comparison, we have used the BBC news dataset that contains text data that can be used for summarization and human generated summaries for evaluating and comparing the summaries generated by machine learning models.

Anjum

Papers

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray.

CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients

Semantic segmentation in medical images through transfused convolution and transformer networks

Study of Fake News Detection using Machine Learning and Deep Learning Classification Methods

Automated News Summarization Using Transformers