Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.
In recent years, the radical advancement of technologies has given rise to an abundance of software applications, social media, and smart devices such as smartphone, sensors, and so on. More extensive use of these applications and tools in various industrial domains has led to data deluge, which has fostered enormous challenges and opportunities. However, it is not only the volume of the data but also the speed, variety, and uncertainty, which are promoting a massive challenge for traditional technologies such as data warehouse. These diverse and unprecedented characteristics have engendered the notion of ''Big Data.'' The data-intensive industries have been experiencing a wide variety of challenges in terms of processing, managing, and analysis of data. For instance, the healthcare sector is confronting difficulties in respect of integration or fusion of diverse medical data stemming from multiple heterogeneous sources. Data integration is critically important within the healthcare sector because it enriches data, enhances its value, and more importantly paves a solid foundation for highly efficient and effective healthcare analytics such as predicting diseases or an outbreak. Several data integration technologies and tools have been developed over the last two decades. This paper aims at studying data integration technologies, tools, and applications within the healthcare domain. Furthermore, this paper discusses future research directions in the integration of Big healthcare data. INDEX TERMS Big data, data integration, healthcare data.
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