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Deep learning‐based applications for disease detection are essential tools for experts to effectively diagnose diseases at different stages. In this article, a new approach based on an evidence based fusion theory is proposed, allowing the combination of a set of deep learning classifiers to provide more accurate disease detection results. The main contribution of this work is the application of the Dempster–Shafer theory for the fusion of five pre trained convolutional neural networks including VGG16, Xception, InceptionV3, ResNet50, and DenseNet201 for the diagnosis of pneumonia from chest X‐ray images. To evaluate this approach, experiments are conducted using a publicly available dataset containing more than 5800 chest X‐ray images. The obtained results demonstrate that our approach provides excellent detection performance compared to other state‐of‐the‐art methods; it achieves a precision of 97.5%, a recall of 98%, an f1‐score of 97.8%, and an accuracy of 97.3%.

By the start of 2020, the novel coronavirus (COVID‐19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID‐19 rapidly and effectively is by analyzing chest X‐ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND‐CNN) architecture for the recognition of COVID‐19. This network consists of a set of differently‐sized hidden layers all created from scratch. The performance of this RND‐CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID‐19 datasets. Each of these datasets consists of medical images (X‐rays) in one of three different classes: chests with COVID‐19, with pneumonia, or in a normal state. The proposed RND‐CNN model yields encouraging results for its accuracy in detecting COVID‐19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID‐19 dataset.

The huge number of deaths caused by the novel pandemic COVID-19, which can affect anyone of any sex, age and socio-demographic status in the world, presents a serious threat for humanity and society. At this point, there are two types of citizens, those oblivious of this contagious disaster's danger that could be one of the causes of its spread, and those who show erratic or even turbulent behavior since fear and anxiety invades our surroundings because of confinement and panic of being affected.In this paper we aim at developing a smart ubiquitous chatbot, called COVID-Chatbot, for COVID-19 assistance during and after quarantine that communicates with a citizen to increase his/her consciousness towards the real danger of this outbreak. Furthermore, COVID-Chatbot is able to recognize and manage stress, during and after lockdown and quarantine period, using natural language processing (NLP).The robust messages delivered from COVID-Chatbot and its way of communication could possibly help to slow the COVID-19 spread.The proposed method is a ubiquitous healthcare service that is presented by its four interdependent modules: Information Understanding Module (IUM) in which the NLP is done, Data Collector Module (DCM) that collect user's non-confidential information to be used later by the Action Generator Module (AGM) that generates the chatbots answers which are managed through its three sub-modules. And finally the Depression Detector Model (DDM) that detects anxiety in the text input through a deep leaning sentiment analysis model to help AGM make the decision to deliver a reassurance message if a bad behavior is distinguished.