CoviLearn: A Machine Learning Integrated Smart X-Ray Device in
Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19
- URL: http://arxiv.org/abs/2106.05861v1
- Date: Wed, 9 Jun 2021 03:40:16 GMT
- Title: CoviLearn: A Machine Learning Integrated Smart X-Ray Device in
Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19
- Authors: Debanjan Das, Chirag Samal, Deewanshu Ukey, Gourav Chowdhary, and
Saraju P. Mohanty
- Abstract summary: We present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS)
We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection.
CoviLearn will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals.
- Score: 0.32622301272834514
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all
over the world causing serious health problems as well as serious impact on the
global economy. Reliable and fast testing of the COVID-19 has been a challenge
for researchers and healthcare practitioners. In this work we present a novel
machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical
System (H-CPS) or smart healthcare framework (called CoviLearn) to allow
healthcare practitioners to perform automatic initial screening of COVID-19
patients. We propose convolutional neural network (CNN) models of X-ray images
integrated into an X-ray device for automatic COVID-19 detection. The proposed
CoviLearn device will be useful in detecting if a person is COVID-19 positive
or negative by considering the chest X-ray image of individuals. CoviLearn will
be useful tool doctors to detect potential COVID-19 infections instantaneously
without taking more intrusive healthcare data samples, such as saliva and
blood. COVID-19 attacks the endothelium tissues that support respiratory tract,
X-rays images can be used to analyze the health of a patient lungs. As all
healthcare centers have X-ray machines, it could be possible to use proposed
CoviLearn X-rays to test for COVID-19 without the especial test kits. Our
proposed automated analysis system CoviLearn which has 99% accuracy will be
able to save valuable time of medical professionals as the X-ray machines come
with a drawback as it needed a radiology expert.
Related papers
- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep
Convolutional Neural Network Design for Detection of COVID-19 Patient Cases
from Point-of-care Ultrasound Imaging [101.27276001592101]
We introduce COVID-Net US, a highly efficient, self-attention deep convolutional neural network design tailored for COVID-19 screening from lung POCUS images.
Experimental results show that the proposed COVID-Net US can achieve an AUC of over 0.98 while achieving 353X lower architectural complexity, 62X lower computational complexity, and 14.3X faster inference times on a Raspberry Pi.
To advocate affordable healthcare and artificial intelligence for resource-constrained environments, we have made COVID-Net US open source and publicly available as part of the COVID-Net open source initiative.
arXiv Detail & Related papers (2021-08-05T16:47:33Z) - Covid-19 diagnosis from x-ray using neural networks [0.0]
Corona virus or COVID-19 is a pandemic illness, which has influenced more than million of causalities worldwide.
This paper proposes a procedure for programmed recognition of COVID-19 from advanced chest X-Ray images.
arXiv Detail & Related papers (2021-05-29T16:12:15Z) - COVID-Net CXR-S: Deep Convolutional Neural Network for Severity
Assessment of COVID-19 Cases from Chest X-ray Images [74.77272804752306]
We introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest.
We leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment.
The proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients.
arXiv Detail & Related papers (2021-05-01T14:15:12Z) - 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: Computer-aided diagnosis of Covid-19 and its severity prediction
with raw digital chest X-ray images [0.6767885381740952]
Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2)
A chest X-ray (CXR) image can be used as an alternative modality to detect and diagnose the COVID-19.
We present an automatic COVID-19 diagnostic and severity prediction (COVIDX) system that uses deep feature maps from CXR images.
arXiv Detail & Related papers (2020-12-25T17:03:06Z) - A New Screening Method for COVID-19 based on Ocular Feature Recognition
by Machine Learning Tools [66.20818586629278]
Coronavirus disease 2019 (COVID-19) has affected several million people.
New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
arXiv Detail & Related papers (2020-09-04T00:50:27Z) - A free web service for fast COVID-19 classification of chest X-Ray
images [5.1263294745542405]
This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques.
The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19.
arXiv Detail & Related papers (2020-08-27T20:53:26Z) - Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray
Images [61.24431480245932]
COVID-19 Multi-Task Network is an automated end-to-end network for COVID-19 screening.
We manually annotate the lung regions of 9000 frontal chest radiographs taken from ChestXray-14, CheXpert and a consolidated COVID-19 dataset.
This database will be released to the research community.
arXiv Detail & Related papers (2020-08-03T12:52:23Z) - CovidAID: COVID-19 Detection Using Chest X-Ray [11.519253771314894]
The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world.
With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR)
We propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing.
We present CovidAID: COVID-19 AI Detector, a novel deep neural network based model to triage patients for appropriate testing.
arXiv Detail & Related papers (2020-04-21T08:02:52Z) - COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using
Chest X-rays [12.100371588940256]
COVID-MobileXpert is a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction.
We employ novel loss functions and training schemes for the MS network to learn the robust features.
arXiv Detail & Related papers (2020-04-06T23:43:58Z) - Review of Artificial Intelligence Techniques in Imaging Data
Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world.
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19.
The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
arXiv Detail & Related papers (2020-04-06T15:21:34Z)
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.