Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest x-rays are the common method used to diagnose coronavirus pneumonia, and it needs a medical expert to evaluate the result of x-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. This work represents a comparison of deep convolutional neural networks models for automatically binary classification query chest x-ray and CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest x-ray and CT open dataset of 5,856 images, and confusion matrices are used to evaluate model performances.

Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models

In today's digital era, Twitter's data has been the focus point among researchers as it provides specific data in a wide variety of fields. Furthermore, Twitter's daily usage has surged throughout the coronavirus disease (COVID-19) period, presenting a unique opportunity to analyze the content and sentiment of COVID-19 tweets. In this paper, a new approach is proposed for the automatic sentiment classification of COVID-19 tweets using the adaptive neuro-fuzzy inference system (ANFIS) models. The entire process includes data collection, pre-processing, word embedding, sentiment analysis, and classification. Many experiments were accomplished to prove the validity and efficiency of the approach using datasets COVID-19 tweets, and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes. The experimental results indicate that fuzzy deep learning achieves the best accuracy (i.e., 0.916) with word embeddings.