Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Optimizing Convolutional Neural Networks with Transfer Learning for Making Classification Report in COVID-19 Chest X-Rays Scans

The coronavirus disease (COVID-19) outbreak, which began in December 2019, has claimed numerous lives and impacted all aspects of human life. COVID-19 was deemed an outbreak by the World Health Organization (WHO) as time passed, putting a tremendous strain on substantially all countries, particularly those with poor health services and delayed reaction times. This recently identified virus is highly contagious. Controlling the rapid spread of this infection requires early detection of infected people through comprehensive screening. For COVID-19 viral diagnosis and follow-up, chest radiography imaging is an excellent tool. Deep learning (DL) has been used for a variety of healthcare purposes, including diabetic retinopathy detection, image classification, and thyroid diagnosis. DL is a useful strategy for combating the COVID-19 outbreak because there are so many streams of medical images (e.g., X-rays, CT, and MRI). In this study, we used the benchmark chest X-ray scan (CXRS) dataset for both COVID-19-infected and noninfected patients. We evaluate the results of DL-based convolutional neural network (CNN) models after preprocessing the scans and using data augmentation. Transfer learning (TL) is used to improve the algorithm's classification performance for chest radiography imaging. Finally, features of the attention and feature interweave modules are combined to create a more accurate feature map. The architecture is trained for COVID-19 CXRS using CNN, and the newly generated feature layer is applied to TL architecture. The experimental results found that training enhances the CNN + TL algorithm's ability to classify CXRS with an overall detection accuracy of 99.3%, precision (0.97), recall (0.98), f-measure (0.98), and receiver operating characteristic (ROC) curve (area = 0.97). The results show that further training improves the classification architecture's performance by 99.3%. [ FROM AUTHOR] Copyright of Scientific Programming is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information.

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.