COVID 19, Pneumonia and Other Disease Classification using Chest X-Ray images
Objective
This is an alternative way of detecting the Covid-19 disease using Convolutional Neural Networks based deep learning models.
Abstract
This study presents a novel approach for the precise classification of COVID-19, pneumonia, and various other respiratory diseases utilizing deep learning models, specifically Convolutional Neural Networks (CNNs). Leveraging a dataset of chest X-ray images, our models exhibit remarkable accuracy in distinguishing COVID-19 cases from other respiratory ailments, including viral and bacterial pneumonia. With an emphasis on achieving high accuracy, our project yielded an impressive performance rate of approximately 98% when tested against a dataset comprising 300 images. This research contributes to the advancement of diagnostic tools for COVID-19, facilitating rapid and accurate identification, which is crucial for effective disease management and containment. The proposed methodology not only demonstrates the potential of deep learning in medical image analysis but also holds promise for enhancing healthcare systems' capabilities in identifying infectious diseases promptly and with precision.
Keywords: Covid-19, Pneumonia, Disease Classification, Accuracy.
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 2020a 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
Related Projects
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, and not projects of or by IEEE.
