LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.
Sachin KumarSourabh ShastriShilpa MahajanKuljeet SinghSurbhi GuptaRajneesh RaniNeeraj MohanVibhakar MansotraPublished in: International journal of imaging systems and technology (2022)
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.
Keyphrases
- coronavirus disease
- sars cov
- deep learning
- artificial intelligence
- early stage
- neural network
- machine learning
- loop mediated isothermal amplification
- big data
- high resolution
- real time pcr
- label free
- radiation therapy
- computed tomography
- optical coherence tomography
- ionic liquid
- lymph node
- magnetic resonance
- intensive care unit
- contrast enhanced
- community acquired pneumonia