Automatic Detection of Coronavirus Disease (Covid-19) in X-Ray and CT Images: A Machine Learning Based Approach
Objective
The objective of this project is to develop a machine learning-based approach for the automatic detection of Coronavirus Disease (COVID-19) in X-ray and CT images. The aim is to design a model that can accurately detect COVID-19 in medical images and distinguish it from other respiratory illnesses with similar symptoms.
Abstract
The
newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly
transmittable and pathogenic with no clinically approved antiviral drug or
vaccine available for treatment. The most common symptoms of COVID-19 are dry
cough, sore throat, and fever. Symptoms can progress to a severe form of
pneumonia with critical complications, including septic shock, pulmonary edema,
acute respiratory distress syndrome and multi-organ failure. While medical
imaging is not currently recommended in Canada for primary diagnosis of
COVID-19, computer-aided diagnosis systems could assist in the early detection
of COVID-19 abnormalities and help to monitor the progression of the disease,
potentially reduce mortality rates. Here we are use Image processing and
machine learning algorithms for classification of COVID 19 Disease using X-ray
and CT images.
Keywords: COVID-19, Image processing, Image processing techniques, Machine learning.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2018a or above
Hardware:
Operating Systems:
· Windows 10
· Windows 7 Service Pack 1
· Windows Server 2019
· Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
