Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device

• URL: http://arxiv.org/abs/2004.06912v1
• Date: Wed, 15 Apr 2020 07:22:02 GMT
• Title: Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
• Authors: Zheng Jiang, Menghan Hu, Lei Fan, Yaling Pan, Wei Tang, Guangtao Zhai, Yong Lu
• Abstract summary: Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. We propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics.
• Score: 39.441555470012965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.

Related papers

• A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19 [64.88701052813462]
  Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets.
  arXiv  Detail & Related papers  (2022-01-13T03:28:20Z)
• COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach [67.05664774727208]
  Chest radiography is an alternative screening method for the COVID-19. Computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights.
  arXiv  Detail & Related papers  (2021-05-04T19:01:40Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review [0.36550217261503676]
  The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus.
  arXiv  Detail & Related papers  (2020-12-10T19:52:12Z)
• Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms [10.232449356645608]
  Non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. frequent contact between users and the cloud might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency.
  arXiv  Detail & Related papers  (2020-11-17T07:33:00Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19 [14.693391992808685]
  Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. This paper investigates deep learning methods for automatically analyzing query chest X-ray images. Deep learning models are proposed to detect pneumonia infection cases, notably viral cases.
  arXiv  Detail & Related papers  (2020-04-05T21:30:54Z)
• Portable Health Screening Device of Respiratory Infections [31.626435815627556]
  The COVID-19 epidemic was listed as a public health emergency of international concern by the WHO on January 30, 2020. Many public places were equipped with thermal imagers to check the body temperature. This demo paper proposes a portable non-contact healthy screening system for people wearing masks.
  arXiv  Detail & Related papers  (2020-04-03T11:40:50Z)
• Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner [38.59200764343499]
  During the epidemic prevention and control period, our study can be helpful in prognosis, diagnosis and screening for the patients infected with COVID-19. Our study can be utilized to distinguish various respiratory patterns and our device can be preliminarily put to practical use.
  arXiv  Detail & Related papers  (2020-02-12T09:42:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.