Covid-19 Detection Using AI

The sudden increase in COVID-19 patients is a major shock to our global health care systems. With limited availability of test kits, it is not possible for all patients with respiratory infections to be tested using RT-PCR. Testing also takes a long time, with limited sensitivity. The detect

Project Title

Covid-19 Detection Using AI

Project Area of Specialization

Artificial Intelligence

Project Summary

The sudden increase in COVID-19 patients is a major shock to our global health care systems. With limited availability of test kits, it is not possible for all patients with respiratory infections to be tested using RT-PCR. Testing also takes a long time, with limited sensitivity. The detection of COVID-19 infections on Chest X-Ray can help isolate patients at high risk while awaiting test results. X-Ray machines are already available in many health care systems, and with many modern X-Ray systems already installed on the computer, there is no travel time involved in the samples. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This can be useful in a hospital setting where current systems have difficulty deciding whether to keep the patient in the ward with other patients or isolate them from COVID-19 areas. It may also be helpful in identifying patients with high risk of COVID with false positive RT-PCR that will require repeated testing. In addition, we recommend the use of modern AI techniques to detect COVID-19 patients who use X-Ray imaging in an automated manner, especially in areas where radiologists are not available, and help make the proposed diagnostic technology easier. Introducing the CovidAID: COVID-19 AI Detector, a model based on a deep neural network of screening patients for proper diagnosis. In a publicly available covid-chest x-ray-dataset .

Project Objectives

TO differentiate  which  patients  with  severe  respiratory infections  (SARI)  may  have  COVID-19  infection.

use  of  chest  X-Ray to detect COVID-19 infection in patients showing SARI symptoms.

To Differentiate common,  bacterial pneumonia,  viral  pneumonia,  and  covid pneumonia.

To identify the accurate result by using Artificial Intelligence

Project Implementation Method

A new database with images of COVID-19 and pneumonia. Both are publicly available on GitHub and Kaggle 

respectively. The Chest X-ray or CT images 

available on GitHub for COVID-19 cases. Created by 

integrating medical images from websites and existing 

public publications. This database contains 204 X-ray 

images of COVID-19. Kaggle's database, on the other 

hand, has been challenged for pneumonia. The 

pictures have binding boxes around the infected areas. 

Samples outside the binding boxes are nutritious and 

contain clear evidence of pneumonia. Samples with 

binding boxes show evidence of pneumonia. We are 

proposing a new database by combining images of 

COVID-19 and pneumonia to find a wide and varied 

one. The fact that they have pneumonia images in the 

training database assumes a greater benefit, due to the common pneumonia and COVID-19 may have similar 

effects on chest X-ray images. This combination of 

data will allow finding a robuster model that can better 

differentiate between those diseases. We will be using the 

network configuration , based on the Single Shot Multibox Detector (SSD). This structure is designed to detect objects in images using a single deep neural network. 

Benefits of the Project

To make the best use of the prescribed resources by differentiating which  patients  with  severe  respiratory infections  (SARI)  may  have  COVID-19  infection.

we  propose the  use  of  chest  X-Ray to detect COVID-19 infection in patients showing SARI symptoms.

The use of  X-Ray has several  advantages over conventional diagnostic tests: 

1. X-ray imaging is more  widespread  and  less expensive  than conventional diagnostic  tests. 
2. X-Ray  digital  image transfer  does  not  require  transfer  from  location  to location analysis, which makes the diagnostic process much  faster.  

3.  Unlike  CT  Scans,  portable  X-Ray machines  also  enable  testing  within  the  isolation ward  itself,  thus  reducing  the  need  for  additional Personal  Protective  Equipment  (PPE)

Technical Details of Final Deliverable

We will be Adopting a three-pronged approach based on testing, classification and tracking of contacts will be allowed to 

combat COVID-19. Itwill be necessary to exploit the 

existing knowledge base to develop effective 

chemotherapeutic agents against COVID-19, taking 

clues from previous studies during other such 

emergencies. All areas ranging from surveillance and 

surveillance to prevention and treatment.

The proposed system will be installed in emergencies and wards specified for covid suspect.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Security

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Zero Hunger

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com