Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging

This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of the same imaging characteristics as other common forms of bacterial and viral pneumonia. Differentiating COVID-19 from other common pulmonary infections is a non-trivial task. To help offset what commonly requires hours of tedious manual annotation, several innovative solutions have been published to help healthcare providers during the COVID-19 pandemic. However, the absence of a comprehensive survey on the subject makes it challenging to ascertain which approaches are promising and therefore deserve further investigation. In this survey, we present an in-depth review of deep learning techniques that have recently been applied to the task of discovering the diagnosis and prognosis of COVID-19 patients. We categorize existing approaches based on features such as dimensionality of radiological imaging, system purpose, and used deep learning techniques, underlying core issues, and challenges. We also address the merits and shortcomings of various approaches, and finally we discuss future directions for this research. [ FROM AUTHOR] Copyright of ACM Computing Surveys is the property of Association for Computing Machinery 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.)

Social media-based COVID-19 sentiment classification model using Bi-LSTM.

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis.

Over the past year, the AI community has constructed several deep learning models for diagnosing COVID-19 based on the visual features of chest X-rays. While deep learning researchers have commonly focused much of their attention on designing deep learning classifiers, only a fraction of these same researchers have dedicated effort to including a segmentation module in their system. This is unfortunate since other applications in radiology typically require segmentation as a necessary prerequisite step in building truly deployable clinical models. Differentiating COVID-19 from other pulmonary diseases can be challenging as various lung diseases share common visual features with COVID-19. To help clarify the diagnosis of suspected COVID-19 patients, we have designed our deep learning pipeline with a segmentation module and ensemble classifier. Following a detailed description of our deep learning pipeline, we present the strengths and shortcomings of our approach and compare our model with other similarly constructed models. While doing so, we focus our attention on widely circulated public datasets and describe several fallacies we have noticed in the literature concerning them. After performing a thorough comparative analysis, we demonstrate that our best model can successfully obtain an accuracy of 91 percent and sensitivity of 92 percent.

Augmented reality, virtual reality and new age technologies demand escalates amid COVID-19

The coronavirus disease (COVID-19) pandemic is having a significant impact on healthcare, education, economics and general human well-being. Augmented reality (AR) and virtual reality (VR) have experienced a tremendous increase in demand as they play an important role in avoiding travel, social contacts and providing adequate audiovisual communication and virtual presence. AR/VR is helping in the fight of this pandemic through their deployment in various crucial areas such as telemedicine, online education and training, marketing and healthcare monitoring. AR/VR provides resolutions for the distribution of antibiotics to control the spread of the COVID-19 virus. This paper discusses the demand for AR and VR as well as other notable new technologies explored recently to support the fight against COVID-19. The same technologies are in high demand, in the form of 2D-3D visualization and tracking software, flow and dynamic wearable headsets, potentially responsible for displaying the geographic location mostly affected with COVID-19.

COV-SNET: A deep learning model for X-ray-based COVID-19 classification.

The AI research community has recently been intensely focused on diagnosing COVID-19 by applying deep learning technology to the X-ray scans taken of COVID-19 patients. Differentiating COVID-19 from other pneumonia-inducing illnesses is a highly challenging task as it shares many of the same imaging characteristics as other pulmonary diseases. This is especially true given the small number of COVID-19 X-rays that are publicly available. Deep learning experts commonly use transfer learning to offset the small number of images typically available in medical imaging tasks. Our COV-SNET model is a deep neural network that was pretrained on over one hundred thousand X-ray images. In this paper, we designed two COV-SNET models with the purpose of diagnosing COVID-19. The experimental results demonstrate the robustness of our deep learning models, ultimately achieving sensitivities of 95% for our three-class and two-class models. We also discuss the strengths and weaknesses of such an approach, focusing mainly on the limitations of public X-ray datasets on current COVID-19 deep learning models. Finally, we conclude with possible future directions for this research.