Detection of Various Lung Diseases Including Covid-19 Using A Lightweight CNN Architecture
Objective
The objective of this study is to develop a lightweight CNN model for efficient, accurate classification of lung diseases like COVID-19 and pneumonia from chest X-rays, optimizing real-time diagnosis capability.
Abstract
The detection of various lung diseases, including COVID-19, has become increasingly important due to the global impact of respiratory disorders. This study proposes a novel approach utilizing a lightweight Convolutional Neural Network (CNN) architecture for feature extraction and classification. The CNN model efficiently captures key spatial features from chest X-ray images, ensuring low computational cost while maintaining high accuracy. These extracted features are fed into the classifier, which is known for its fast learning speed and generalization ability. The proposed method is evaluated on publicly available datasets of lung diseases, demonstrating its effectiveness in distinguishing between COVID-19, pneumonia, tuberculosis, and other lung conditions. Experimental results indicate that the hybrid CNN model achieves competitive classification performance with a significant reduction in processing time, making it a viable solution for real-time medical diagnosis.
Keywords: Lung disease, COVID- 19, Convolutional Neural Network, X-ray images.
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
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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.
